Coral Physiology Following Reciprocal Transplant
Serena Hackerott
1/1/2024
Setup
Load Packages
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("lme4")) install.packages("lme4")
if (!require("vegan")) install.packages("vegan")
if (!require("corrplot")) install.packages("corrplot")
if (!require("DHARMa")) install.packages("DHARMa")
if (!require("effectsize")) install.packages("effectsize")
if (!require("emmeans")) install.packages("emmeans")
if (!require("Hmisc")) install.packages("Hmisc")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")
##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(Rmisc) #Required for SummarySE function
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(corrplot) #Required for correlation plot
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons
library(Hmisc) #Required for correlations
library(dplyr) #Required for dataframe organization
library(tidyr) #Required for dataframe organization
Graphing Parameters
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd fileSample Data and Metadata
Load and Organize Data
##Load Data
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Coral<-read.csv("Outputs/CoralData.csv", header=TRUE)
##Set factor variables
Coral$TimeP<-factor(Coral$TimeP, levels=c("TP1", "TP2", "TP3", "TP4"))
Coral$Site<-factor(Coral$Site, levels=c("KL", "SS"))
Coral$Genotype<-factor(Coral$Genotype, levels=c("AC8", "AC10", "AC12"))
Coral$Orig<-factor(Coral$Orig, levels=c("N", "T"))
Coral$Origin<-factor(Coral$Origin, levels=c("Native", "Transplant"))
Coral$Site.Orig<-factor(Coral$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))
Multivariate Analysis
adonis OmegaSq Function
#' Calculate (partial) Omega-squared (effect-size calculation) for PERMANOVA and add it to the input object
#'
#' @param adonisOutput An adonis object
#' @param partial Should partial omega-squared be calculated (sample size adjusted). Default TRUE
#' @return Original adonis object with the (partial) Omega-squared values added
#' @import vegan
#' @export
adonis_OmegaSq <- function(adonisOutput, partial = TRUE){
if(!(is(adonisOutput, "adonis") || is(adonisOutput, "anova.cca")))
stop("Input should be an adonis object")
if (is(adonisOutput, "anova.cca")) {
aov_tab <- adonisOutput
aov_tab$MeanSqs <- aov_tab$SumOfSqs / aov_tab$Df
aov_tab$MeanSqs[length(aov_tab$Df)] <- NA
} else {
aov_tab <- adonisOutput$aov.tab
}
heading <- attr(aov_tab, "heading")
MS_res <- aov_tab[pmatch("Residual", rownames(aov_tab)), "MeanSqs"]
SS_tot <- aov_tab[rownames(aov_tab) == "Total", "SumsOfSqs"]
N <- aov_tab[rownames(aov_tab) == "Total", "Df"] + 1
if(partial){
omega <- apply(aov_tab, 1, function(x) (x["Df"]*(x["MeanSqs"]-MS_res))/(x["Df"]*x["MeanSqs"]+(N-x["Df"])*MS_res))
aov_tab$parOmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
} else {
omega <- apply(aov_tab, 1, function(x) (x["SumsOfSqs"]-x["Df"]*MS_res)/(SS_tot+MS_res))
aov_tab$OmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
}
if (is(adonisOutput, "adonis"))
cn_order <- c("Df", "SumsOfSqs", "MeanSqs", "F.Model", "R2",
if (partial) "parOmegaSq" else "OmegaSq", "Pr(>F)")
else
cn_order <- c("Df", "SumOfSqs", "F", if (partial) "parOmegaSq" else "OmegaSq",
"Pr(>F)")
aov_tab <- aov_tab[, cn_order]
attr(aov_tab, "names") <- cn_order
attr(aov_tab, "heading") <- heading
if (is(adonisOutput, "adonis"))
adonisOutput$aov.tab <- aov_tab
else
adonisOutput <- aov_tab
return(adonisOutput)
}Check Correlation
##Remove NA's
names(Coral) [1] "ID" "RandN" "TimeP" "Site" "Genotype"
[6] "Orig" "Origin" "Set" "Site.Orig" "SA_cm2"
[11] "TP_ug.cm2_C" "TP_ug.cm2_S" "AFDW_mg.cm2_C" "AFDW_mg.cm2_S" "AFDW_mg.cm2_S.C"
[16] "Chl_ug.cm2" Coral.rm<-na.omit(Coral)
##Log +1 transform
Coral.log<-Coral.rm
Coral.log[,-c(1:10)]<-log(Coral.rm[,-c(1:10)]+1)
Phys.cor<-rcorr(as.matrix(Coral.log[,-c(1:10)]), type="pearson")
Phys.cor TP_ug.cm2_C TP_ug.cm2_S AFDW_mg.cm2_C AFDW_mg.cm2_S AFDW_mg.cm2_S.C Chl_ug.cm2
TP_ug.cm2_C 1.00 0.56 0.55 0.48 0.03 0.37
TP_ug.cm2_S 0.56 1.00 0.37 0.41 0.08 0.16
AFDW_mg.cm2_C 0.55 0.37 1.00 0.37 -0.49 0.33
AFDW_mg.cm2_S 0.48 0.41 0.37 1.00 0.60 0.50
AFDW_mg.cm2_S.C 0.03 0.08 -0.49 0.60 1.00 0.20
Chl_ug.cm2 0.37 0.16 0.33 0.50 0.20 1.00
n= 190
P
TP_ug.cm2_C TP_ug.cm2_S AFDW_mg.cm2_C AFDW_mg.cm2_S AFDW_mg.cm2_S.C Chl_ug.cm2
TP_ug.cm2_C 0.0000 0.0000 0.0000 0.6401 0.0000
TP_ug.cm2_S 0.0000 0.0000 0.0000 0.2762 0.0248
AFDW_mg.cm2_C 0.0000 0.0000 0.0000 0.0000 0.0000
AFDW_mg.cm2_S 0.0000 0.0000 0.0000 0.0000 0.0000
AFDW_mg.cm2_S.C 0.6401 0.2762 0.0000 0.0000 0.0049
Chl_ug.cm2 0.0000 0.0248 0.0000 0.0000 0.0049 diag(Phys.cor$P)<-0
corrplot(Phys.cor$r, type="upper", order="hclust",
p.mat = Phys.cor$P, sig.level = 0.01, insig = "blank")
TP1
##Subset Timepoint 1
Coral.log_TP1<-subset(Coral.log, TimeP=="TP1")
PERMANOVA
##PERMANOVA
Coral.TP1.perm<-adonis2(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data=Coral.log_TP1, strata=Coral.log_TP1$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[, c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data = Coral.log_TP1, method = "euclidean", strata = Coral.log_TP1$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP1$Origin 1 0.2789 2.7121 0.034440 0.015 *
Coral.log_TP1$Site 1 0.3722 3.6196 0.051750 0.001 ***
Coral.log_TP1$Origin:Coral.log_TP1$Site 1 0.3433 3.3384 0.046454 0.006 **
Residual 44 4.5240
Total 47 5.5183
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP1.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin))
TP1.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.01547 0.015468 1.0199 0.3178
Residuals 46 0.69768 0.015167 ##Check dispersion by Site
TP1.disp_site<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Site))
TP1.disp_siteAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.04842 0.048424 3.1669 0.08175 .
Residuals 46 0.70338 0.015291
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin:Site
TP1.disp_os<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin:Coral.log_TP1$Site))
TP1.disp_osAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.02212 0.0073726 0.5649 0.641
Residuals 44 0.57425 0.0130512 ##Save results
PERM_T1.res<-data.frame(adonis_OmegaSq(Coral.TP1.perm)[1:3,])
PERM_T1.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T1.res$p_DISP<-c(TP1.disp_orig$`Pr(>F)`[1], TP1.disp_site$`Pr(>F)`[1], TP1.disp_os$`Pr(>F)`[1])Physiology differs significantly by Origin and the effect of Origin differs between Sites.
Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP1.perm_KL<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[which(Coral.log_TP1$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "KL")], data = Coral.log_TP1[which(Coral.log_TP1$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP1$Genotype[which(Coral.log_TP1$Site == "KL")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "KL")] 1 0.05913 0.6453 -0.015001 0.413
Residual 22 2.01586
Total 23 2.07498 ##Check dispersion by Origin
TP1.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")]))
TP1.KL.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.002019 0.0020194 0.1424 0.7095
Residuals 22 0.311986 0.0141812 ##SS
##PERMANOVA
Coral.TP1.perm_SS<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP1.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP1[which(Coral.log_TP1$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "SS")], data = Coral.log_TP1[which(Coral.log_TP1$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP1$Genotype[which(Coral.log_TP1$Site == "SS")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP1$Origin[which(Coral.log_TP1$Site == "SS")] 1 0.56298 4.9381 0.14096 0.001 ***
Residual 22 2.50815
Total 23 3.07113
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP1.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")]))
TP1.SS.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.000018 0.0000178 0.0015 0.9695
Residuals 22 0.262265 0.0119211 ##Save results
PERM_T1.site.res<-data.frame(adonis_OmegaSq(Coral.TP1.perm_KL)[1,])
PERM_T1.site.res<-rbind(PERM_T1.site.res, data.frame(adonis_OmegaSq(Coral.TP1.perm_SS)[1,]))
PERM_T1.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T1.site.res$p_DISP<-c(TP1.KL.disp_orig$`Pr(>F)`[1], TP1.SS.disp_orig$`Pr(>F)`[1])
##Combine results
PERM_T1.res<-rbind(PERM_T1.res, PERM_T1.site.res)
PERM_T1.res$Timepoint<-rep("TP1", nrow(PERM_T1.res))
TP2
##Subset Timepoint 2
Coral.log_TP2<-subset(Coral.log, TimeP=="TP2")
PERMANOVA
##PERMANOVA
Coral.TP2.perm<-adonis2(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data=Coral.log_TP2, strata=Coral.log_TP2$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[, c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data = Coral.log_TP2, method = "euclidean", strata = Coral.log_TP2$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP2$Origin 1 0.0347 0.420 -0.01223 0.686
Coral.log_TP2$Site 1 2.0831 25.239 0.33554 0.001 ***
Coral.log_TP2$Origin:Coral.log_TP2$Site 1 0.2762 3.346 0.04660 0.027 *
Residual 44 3.6315
Total 47 6.0255
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP2.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin))
TP2.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.02892 0.028918 1.1826 0.2825
Residuals 46 1.12482 0.024453 ##Check dispersion by Site
TP2.disp_site<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Site))
TP2.disp_siteAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00701 0.0070126 0.5917 0.4457
Residuals 46 0.54518 0.0118518 ##Check dispersion by Origin:Site
TP2.disp_os<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin:Coral.log_TP2$Site))
TP2.disp_osAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.02003 0.0066763 0.6202 0.6057
Residuals 44 0.47364 0.0107645 ##Save results
PERM_T2.res<-data.frame(adonis_OmegaSq(Coral.TP2.perm)[1:3,])
PERM_T2.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T2.res$p_DISP<-c(TP2.disp_orig$`Pr(>F)`[1], TP2.disp_site$`Pr(>F)`[1], TP2.disp_os$`Pr(>F)`[1])
Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP2.perm_KL<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[which(Coral.log_TP2$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "KL")], data = Coral.log_TP2[which(Coral.log_TP2$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP2$Genotype[which(Coral.log_TP2$Site == "KL")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "KL")] 1 0.18146 2.5268 0.059813 0.043 *
Residual 22 1.57991
Total 23 1.76137
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP2.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")]))
TP2.KL.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.000732 0.0007320 0.0896 0.7675
Residuals 22 0.179698 0.0081681 ##SS
##PERMANOVA
Coral.TP2.perm_SS<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP2.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP2[which(Coral.log_TP2$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "SS")], data = Coral.log_TP2[which(Coral.log_TP2$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP2$Genotype[which(Coral.log_TP2$Site == "SS")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP2$Origin[which(Coral.log_TP2$Site == "SS")] 1 0.12937 1.3872 0.015878 0.158
Residual 22 2.05163
Total 23 2.18100 ##Check dispersion by Origin
TP2.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")]))
TP2.SS.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.006276 0.0062764 0.4698 0.5003
Residuals 22 0.293942 0.0133610 ##Save results
PERM_T2.site.res<-data.frame(adonis_OmegaSq(Coral.TP2.perm_KL)[1,])
PERM_T2.site.res<-rbind(PERM_T2.site.res, data.frame(adonis_OmegaSq(Coral.TP2.perm_SS)[1,]))
PERM_T2.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T2.site.res$p_DISP<-c(TP2.KL.disp_orig$`Pr(>F)`[1], TP2.SS.disp_orig$`Pr(>F)`[1])
##Combine results
PERM_T2.res<-rbind(PERM_T2.res, PERM_T2.site.res)
PERM_T2.res$Timepoint<-rep("TP2", nrow(PERM_T2.res))
TP3
##Subset Timepoint 3
Coral.log_TP3<-subset(Coral.log, TimeP=="TP3")
PERMANOVA
##PERMANOVA
Coral.TP3.perm<-adonis2(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data=Coral.log_TP3, strata=Coral.log_TP3$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[, c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data = Coral.log_TP3, method = "euclidean", strata = Coral.log_TP3$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP3$Origin 1 0.1051 0.7853 -0.004493 0.344
Coral.log_TP3$Site 1 1.4021 10.4738 0.164837 0.001 ***
Coral.log_TP3$Origin:Coral.log_TP3$Site 1 0.1024 0.7649 -0.004921 0.372
Residual 44 5.8901
Total 47 7.4997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP3.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin))
TP3.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.06528 0.065278 2.0695 0.157
Residuals 46 1.45099 0.031543 ##Check dispersion by Site
TP3.disp_site<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Site))
TP3.disp_siteAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00202 0.002024 0.0609 0.8062
Residuals 46 1.52934 0.033246 ##Check dispersion by Origin:Site
TP3.disp_os<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin:Coral.log_TP3$Site))
TP3.disp_osAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.09225 0.030751 0.975 0.4131
Residuals 44 1.38769 0.031538 ##Save results
PERM_T3.res<-data.frame(adonis_OmegaSq(Coral.TP3.perm)[1:3,])
PERM_T3.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T3.res$p_DISP<-c(TP3.disp_orig$`Pr(>F)`[1], TP3.disp_site$`Pr(>F)`[1], TP3.disp_os$`Pr(>F)`[1])
Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP3.perm_KL<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[which(Coral.log_TP3$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "KL")], data = Coral.log_TP3[which(Coral.log_TP3$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP3$Genotype[which(Coral.log_TP3$Site == "KL")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "KL")] 1 0.17515 1.5145 0.020987 0.179
Residual 22 2.54431
Total 23 2.71946 ##Check dispersion by Origin
TP3.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")]))
TP3.KL.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00703 0.0070337 0.3193 0.5778
Residuals 22 0.48467 0.0220307 ##SS
##PERMANOVA
Coral.TP3.perm_SS<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP3.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP3[which(Coral.log_TP3$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "SS")], data = Coral.log_TP3[which(Coral.log_TP3$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP3$Genotype[which(Coral.log_TP3$Site == "SS")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP3$Origin[which(Coral.log_TP3$Site == "SS")] 1 0.0324 0.2129 -0.03391 0.782
Residual 22 3.3458
Total 23 3.3782 ##Check dispersion by Origin
TP3.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")]))
TP3.SS.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.07949 0.079485 1.9365 0.178
Residuals 22 0.90301 0.041046 ##Save results
PERM_T3.site.res<-data.frame(adonis_OmegaSq(Coral.TP3.perm_KL)[1,])
PERM_T3.site.res<-rbind(PERM_T3.site.res, data.frame(adonis_OmegaSq(Coral.TP3.perm_SS)[1,]))
PERM_T3.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T3.site.res$p_DISP<-c(TP3.KL.disp_orig$`Pr(>F)`[1], TP3.SS.disp_orig$`Pr(>F)`[1])
##Combine results
PERM_T3.res<-rbind(PERM_T3.res, PERM_T3.site.res)
PERM_T3.res$Timepoint<-rep("TP3", nrow(PERM_T3.res))
TP4
##Subset Timepoint 4
Coral.log_TP4<-subset(Coral.log, TimeP=="TP4")
PERMANOVA
##PERMANOVA
Coral.TP4.perm<-adonis2(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data=Coral.log_TP4, strata=Coral.log_TP4$Genotype, method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[, c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data = Coral.log_TP4, method = "euclidean", strata = Coral.log_TP4$Genotype)
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP4$Origin 1 0.0204 0.1301 -0.019276 0.940
Coral.log_TP4$Site 1 2.5446 16.2273 0.248701 0.001 ***
Coral.log_TP4$Origin:Coral.log_TP4$Site 1 0.2287 1.4586 0.009870 0.178
Residual 42 6.5861
Total 45 9.3799
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1##Check dispersion by Origin
TP4.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin))
TP4.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00639 0.006388 0.1952 0.6608
Residuals 44 1.44022 0.032732 ##Check dispersion by Site
TP4.disp_site<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Site))
TP4.disp_siteAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.01153 0.011529 0.3704 0.5459
Residuals 44 1.36932 0.031121 ##Check dispersion by Origin:Site
TP4.disp_os<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin:Coral.log_TP4$Site))
TP4.disp_osAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 3 0.04203 0.014012 0.4161 0.7423
Residuals 42 1.41428 0.033673 ##Save results
PERM_T4.res<-data.frame(adonis_OmegaSq(Coral.TP4.perm)[1:3,])
PERM_T4.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T4.res$p_DISP<-c(TP4.disp_orig$`Pr(>F)`[1], TP4.disp_site$`Pr(>F)`[1], TP4.disp_os$`Pr(>F)`[1])
Effect Size
Calculate Effect Size of Origin for each Site
##KL
##PERMANOVA
Coral.TP4.perm_KL<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="KL")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm_KL)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[which(Coral.log_TP4$Site == "KL"), c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "KL")], data = Coral.log_TP4[which(Coral.log_TP4$Site == "KL"), ], method = "euclidean", strata = Coral.log_TP4$Genotype[which(Coral.log_TP4$Site == "KL")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "KL")] 1 0.0676 0.3774 -0.027823 0.663
Residual 21 3.7601
Total 22 3.8277 ##Check dispersion by Origin
TP4.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")]))
TP4.KL.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.00316 0.003160 0.0776 0.7833
Residuals 21 0.85503 0.040716 ##SS
##PERMANOVA
Coral.TP4.perm_SS<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="SS")], method="euclidean")
##Effect Size
adonis_OmegaSq(Coral.TP4.perm_SS)Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Blocks: strata
Permutation: free
Number of permutations: 999
adonis2(formula = vegdist(Coral.log_TP4[which(Coral.log_TP4$Site == "SS"), c(11:14, 16)], "euclidean") ~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "SS")], data = Coral.log_TP4[which(Coral.log_TP4$Site == "SS"), ], method = "euclidean", strata = Coral.log_TP4$Genotype[which(Coral.log_TP4$Site == "SS")])
Df SumOfSqs F parOmegaSq Pr(>F)
Coral.log_TP4$Origin[which(Coral.log_TP4$Site == "SS")] 1 0.18154 1.349 0.014948 0.156
Residual 21 2.82599
Total 22 3.00753 ##Check dispersion by Origin
TP4.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")]))
TP4.SS.disp_origAnalysis of Variance Table
Response: Distances
Df Sum Sq Mean Sq F value Pr(>F)
Groups 1 0.02157 0.021572 0.81 0.3783
Residuals 21 0.55925 0.026631 ##Save results
PERM_T4.site.res<-data.frame(adonis_OmegaSq(Coral.TP4.perm_KL)[1,])
PERM_T4.site.res<-rbind(PERM_T4.site.res, data.frame(adonis_OmegaSq(Coral.TP4.perm_SS)[1,]))
PERM_T4.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T4.site.res$p_DISP<-c(TP4.KL.disp_orig$`Pr(>F)`[1], TP4.SS.disp_orig$`Pr(>F)`[1])
##Combine results
PERM_T4.res<-rbind(PERM_T4.res, PERM_T4.site.res)
PERM_T4.res$Timepoint<-rep("TP4", nrow(PERM_T4.res))
Univariate
TP1
##Subset Timepoint 1
Coral.TP1<-subset(Coral.rm, TimeP=="TP1")
Protein Host
##Check normality
hist(Coral.TP1$TP_ug.cm2_C)
shapiro.test(Coral.TP1$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP1$TP_ug.cm2_C
W = 0.98637, p-value = 0.8448#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP1<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Prot.C.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.C.lme_TP1, plot = F)
plot(Prot.C.lme_res_TP1)
##Model results
summary(Prot.C.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 476.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.98711 -0.58176 -0.00143 0.76130 1.61635
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1703 41.27
Residual 2107 45.91
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 340.714 27.262 2.948 12.498 0.00120 **
OriginTransplant 3.303 18.741 42.000 0.176 0.86096
SiteSS 55.261 18.741 42.000 2.949 0.00519 **
OriginTransplant:SiteSS -31.334 26.504 42.000 -1.182 0.24375
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.344
SiteSS -0.344 0.500
OrgnTrn:SSS 0.243 -0.707 -0.707eta_squared(Prot.C.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.18 | [0.04, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.C_TP1.res<-data.frame(summary(Prot.C.lme_TP1)$coefficients[-1,])
Prot.C_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP1.res$EtaSq<-c(eta_squared(Prot.C.lme_TP1)$Eta2)
Prot.C_TP1.res$Response<-rep("Protein Host", nrow(Prot.C_TP1.res))Protein Symbiont
##Check normality
hist(Coral.TP1$TP_ug.cm2_S)
shapiro.test(Coral.TP1$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP1$TP_ug.cm2_S
W = 0.96018, p-value = 0.1027#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP1<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Prot.S.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.S.lme_TP1, plot = F)
plot(Prot.S.lme_res_TP1)
##Model results
summary(Prot.S.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 504.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.99771 -0.58981 -0.06134 0.48297 2.49136
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1613 40.17
Residual 4087 63.93
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 459.014 29.638 3.945 15.488 0.000111 ***
OriginTransplant 1.258 26.100 42.000 0.048 0.961800
SiteSS 42.145 26.100 42.000 1.615 0.113851
OriginTransplant:SiteSS -86.531 36.910 42.000 -2.344 0.023864 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.440
SiteSS -0.440 0.500
OrgnTrn:SSS 0.311 -0.707 -0.707eta_squared(Prot.S.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.11 | [0.01, 1.00]
Site | 8.78e-05 | [0.00, 1.00]
Origin:Site | 0.12 | [0.01, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.S_TP1.res<-data.frame(summary(Prot.S.lme_TP1)$coefficients[-1,])
Prot.S_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP1.res$EtaSq<-c(eta_squared(Prot.S.lme_TP1)$Eta2)
Prot.S_TP1.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP1.res))Effect size of Origin for each Site
##KL
Prot.S.lme_TP1_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Prot.S.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: 254.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.64984 -0.68376 -0.07079 0.30913 2.31492
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 2602 51.01
Residual 4223 64.99
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 459.014 34.918 2.724 13.145 0.00153 **
OriginTransplant 1.258 26.531 20.000 0.047 0.96267
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.380eta_squared(Prot.S.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 1.12e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP1_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Prot.S.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 251.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.7405 -0.5819 -0.2580 0.6750 2.1154
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 695.5 26.37
Residual 3899.6 62.45
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 501.158 23.596 3.921 21.239 3.4e-05 ***
OriginTransplant -85.273 25.494 20.000 -3.345 0.00323 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.540eta_squared(Prot.S.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.36 | [0.10, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Prot.S_TP1.site.res<-data.frame(rbind(summary(Prot.S.lme_TP1_KL)$coefficients[-1,],
summary(Prot.S.lme_TP1_SS)$coefficients[-1,]))
Prot.S_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP1.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP1_KL)$Eta2, eta_squared(Prot.S.lme_TP1_SS)$Eta2)
Prot.S_TP1.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP1.site.res))
##Combine results
Prot.S_TP1.res<-rbind(Prot.S_TP1.res, Prot.S_TP1.site.res)
##Summary statistics by Site and Origin
TP1_ProtSym.sum<-summarySE(Coral.TP1, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP1_ProtSym.plot<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(250, 550)+
annotate("text", x=2, y=535, label="**", size=sig.sz, fontface="bold"); TP1_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_C)
shapiro.test(Coral.TP1$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP1$AFDW_mg.cm2_C
W = 0.96547, p-value = 0.1677#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP1<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Bio.C.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.C.lme_TP1, plot = F)
plot(Bio.C.lme_res_TP1)
##Model results
summary(Bio.C.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: 17.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.3616 -0.6323 0.1012 0.6827 1.4522
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006352 0.0797
Residual 0.067274 0.2594
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.20320 0.08788 9.02171 13.691 2.43e-07 ***
OriginTransplant -0.18538 0.10589 42.00000 -1.751 0.0873 .
SiteSS -0.01392 0.10589 42.00000 -0.131 0.8961
OriginTransplant:SiteSS 0.04772 0.14975 42.00000 0.319 0.7516
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.602
SiteSS -0.602 0.500
OrgnTrn:SSS 0.426 -0.707 -0.707eta_squared(Bio.C.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.10 | [0.00, 1.00]
Site | 4.20e-04 | [0.00, 1.00]
Origin:Site | 2.41e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.C_TP1.res<-data.frame(summary(Bio.C.lme_TP1)$coefficients[-1,])
Bio.C_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP1.res$EtaSq<-c(eta_squared(Bio.C.lme_TP1)$Eta2)
Bio.C_TP1.res$Response<-rep("Biomass Host", nrow(Bio.C_TP1.res))
Effect size of Origin for each Site
##KL
Bio.C.lme_TP1_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.C.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: 1.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.61169 -0.43907 0.07185 0.60072 1.71208
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.003322 0.05764
Residual 0.047206 0.21727
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.2032 0.0710 5.1663 16.95 9.99e-06 ***
OriginTransplant -0.1854 0.0887 20.0000 -2.09 0.0496 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.625eta_squared(Bio.C.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.18 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP1_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.C.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 15.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.9881 -0.5903 0.3895 0.6868 1.3845
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0002328 0.01526
Residual 0.0939949 0.30659
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.18928 0.08894 7.15744 13.37 2.53e-06 ***
OriginTransplant -0.13767 0.12516 20.00012 -1.10 0.284
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.704eta_squared(Bio.C.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.C_TP1.site.res<-data.frame(rbind(summary(Bio.C.lme_TP1_KL)$coefficients[-1,],
summary(Bio.C.lme_TP1_SS)$coefficients[-1,]))
Bio.C_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP1.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP1_KL)$Eta2, eta_squared(Bio.C.lme_TP1_SS)$Eta2)
Bio.C_TP1.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP1.site.res))
##Combine results
Bio.C_TP1.res<-rbind(Bio.C_TP1.res, Bio.C_TP1.site.res)
##Summary statistics by Site and Origin
TP1_BioHost.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP1_BioHost.plot<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position = "top")+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.8, 1.6)+
annotate("text", x=1, y=1.3, label="*", size=sig.sz, fontface="bold"); TP1_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_S)
shapiro.test(Coral.TP1$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP1$AFDW_mg.cm2_S
W = 0.97584, p-value = 0.4194#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP1<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Bio.S.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.S.lme_TP1, plot = F)
plot(Bio.S.lme_res_TP1)
##Model results
summary(Bio.S.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: -29.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.71364 -0.74817 -0.06341 0.60227 2.13813
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002785 0.05277
Residual 0.022713 0.15071
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.57323 0.05311 7.72575 10.792 6.25e-06 ***
OriginTransplant 0.01058 0.06153 42.00000 0.172 0.8642
SiteSS 0.16334 0.06153 42.00000 2.655 0.0112 *
OriginTransplant:SiteSS -0.22488 0.08701 42.00000 -2.584 0.0133 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.579
SiteSS -0.579 0.500
OrgnTrn:SSS 0.410 -0.707 -0.707eta_squared(Bio.S.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.12 | [0.01, 1.00]
Site | 0.03 | [0.00, 1.00]
Origin:Site | 0.14 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.S_TP1.res<-data.frame(summary(Bio.S.lme_TP1)$coefficients[-1,])
Bio.S_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP1.res$EtaSq<-c(eta_squared(Bio.S.lme_TP1)$Eta2)
Bio.S_TP1.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP1.res))
Effect size of Origin for each Site
##KL
Bio.S.lme_TP1_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.S.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: -21.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.72868 -0.71431 0.04303 0.52737 1.90341
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01040 0.1020
Residual 0.01508 0.1228
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.57323 0.06873 2.65466 8.340 0.00563 **
OriginTransplant 0.01058 0.05014 20.00000 0.211 0.83496
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.365eta_squared(Bio.S.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.22e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP1_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.S.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: -13.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.2456 -0.7860 -0.1378 0.6402 2.1806
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004289 0.06549
Residual 0.023707 0.15397
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.73657 0.05836 3.90305 12.622 0.000262 ***
OriginTransplant -0.21429 0.06286 20.00000 -3.409 0.002782 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.539eta_squared(Bio.S.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.37 | [0.10, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.S_TP1.site.res<-data.frame(rbind(summary(Bio.S.lme_TP1_KL)$coefficients[-1,],
summary(Bio.S.lme_TP1_SS)$coefficients[-1,]))
Bio.S_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP1.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP1_KL)$Eta2, eta_squared(Bio.S.lme_TP1_SS)$Eta2)
Bio.S_TP1.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP1.site.res))
##Combine results
Bio.S_TP1.res<-rbind(Bio.S_TP1.res, Bio.S_TP1.site.res)
##Summary statistics by Site and Origin
TP1_BioSym.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP1_BioSym.plot<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.45, 0.95)+
annotate("text", x=2, y=0.8, label="**", size=sig.sz, fontface="bold"); TP1_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP1$Chl_ug.cm2)
shapiro.test(Coral.TP1$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP1$Chl_ug.cm2
W = 0.95564, p-value = 0.06727#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP1<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP1)
##Check residuals
Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Chl.lme_TP1, plot = F)
plot(Chl.lme_res_TP1)
##Model results
summary(Chl.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin * Site + (1 | Genotype)
Data: Coral.TP1
REML criterion at convergence: -0.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.1377 -0.5871 -0.1548 0.6976 2.0162
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.08669 0.2944
Residual 0.03936 0.1984
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.86074 0.17937 2.34405 4.799 0.029564 *
OriginTransplant 0.07568 0.08099 42.00000 0.934 0.355451
SiteSS 0.54382 0.08099 42.00000 6.715 3.73e-08 ***
OriginTransplant:SiteSS -0.48115 0.11454 42.00000 -4.201 0.000136 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.226
SiteSS -0.226 0.500
OrgnTrn:SSS 0.160 -0.707 -0.707eta_squared(Chl.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.16 | [0.03, 1.00]
Site | 0.40 | [0.21, 1.00]
Origin:Site | 0.30 | [0.12, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Chl_TP1.res<-data.frame(summary(Chl.lme_TP1)$coefficients[-1,])
Chl_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP1.res$EtaSq<-c(eta_squared(Chl.lme_TP1)$Eta2)
Chl_TP1.res$Response<-rep("Chlorophyll", nrow(Chl_TP1.res))
Effect size of Origin for each Site
##KL
Chl.lme_TP1_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Chl.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "KL"), ]
REML criterion at convergence: -4.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.42282 -0.68856 0.00126 0.53926 2.32168
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.05037 0.2244
Residual 0.02931 0.1712
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.86074 0.13868 2.27932 6.207 0.018 *
OriginTransplant 0.07568 0.06989 20.00000 1.083 0.292
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.252eta_squared(Chl.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP1_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Chl.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP1[which(Coral.TP1$Site == "SS"), ]
REML criterion at convergence: 2.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.0949 -0.5799 0.1160 0.6297 1.7750
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.13767 0.3710
Residual 0.03874 0.1968
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.40456 0.22163 2.13798 6.338 0.0203 *
OriginTransplant -0.40548 0.08035 20.00000 -5.046 6.18e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.181eta_squared(Chl.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.56 | [0.30, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Chl_TP1.site.res<-data.frame(rbind(summary(Chl.lme_TP1_KL)$coefficients[-1,],
summary(Chl.lme_TP1_SS)$coefficients[-1,]))
Chl_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP1.site.res$EtaSq<-c(eta_squared(Chl.lme_TP1_KL)$Eta2, eta_squared(Chl.lme_TP1_SS)$Eta2)
Chl_TP1.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP1.site.res))
##Combine results
Chl_TP1.res<-rbind(Chl_TP1.res, Chl_TP1.site.res)
##Summary statistics by Site and Origin
TP1_Chl.sum<-summarySE(Coral.TP1, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP1_Chl.plot<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(0.5, 3.25)+
annotate("text", x=2, y=1.7, label="***", size=sig.sz, fontface="bold"); TP1_Chl.plot
Save Results
##Combine Results
Phys_T1.res<-rbind(Prot.C_TP1.res, Prot.S_TP1.res, Bio.C_TP1.res, Bio.S_TP1.res, Chl_TP1.res)
##Add Timepoint
Phys_T1.res$TimeP<-rep("TP1", nrow(Phys_T1.res))
TP2
##Subset Timepoint 2
Coral.TP2<-subset(Coral.rm, TimeP=="TP2")
Protein Host
##Check normality
hist(Coral.TP2$TP_ug.cm2_C)
shapiro.test(Coral.TP2$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP2$TP_ug.cm2_C
W = 0.9796, p-value = 0.562#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP2<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Prot.C.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.C.lme_TP2, plot = F)
plot(Prot.C.lme_res_TP2)
##Model results
summary(Prot.C.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 469.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.67310 -0.67673 -0.00324 0.57592 1.67096
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 391.9 19.80
Residual 1877.0 43.32
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 315.853 16.942 5.593 18.643 3e-06 ***
OriginTransplant -1.965 17.687 42.000 -0.111 0.9121
SiteSS 42.263 17.687 42.000 2.389 0.0214 *
OriginTransplant:SiteSS 8.579 25.013 42.000 0.343 0.7333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.522
SiteSS -0.522 0.500
OrgnTrn:SSS 0.369 -0.707 -0.707eta_squared(Prot.C.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 8.22e-04 | [0.00, 1.00]
Site | 0.25 | [0.08, 1.00]
Origin:Site | 2.79e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.C_TP2.res<-data.frame(summary(Prot.C.lme_TP2)$coefficients[-1,])
Prot.C_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP2.res$EtaSq<-c(eta_squared(Prot.C.lme_TP2)$Eta2)
Prot.C_TP2.res$Response<-rep("Protein Host", nrow(Prot.C_TP2.res))
Protein Symbiont
##Check normality
hist(Coral.TP2$TP_ug.cm2_S)
shapiro.test(Coral.TP2$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP2$TP_ug.cm2_S
W = 0.95982, p-value = 0.09929#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP2<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Prot.S.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.S.lme_TP2, plot = F)
plot(Prot.S.lme_res_TP2)
##Model results
summary(Prot.S.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 493.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.5114 -0.6809 -0.1247 0.6145 2.6570
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 796.1 28.22
Residual 3198.1 56.55
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 329.068 23.062 5.046 14.269 2.85e-05 ***
OriginTransplant -29.475 23.087 42.000 -1.277 0.209
SiteSS 43.245 23.087 42.000 1.873 0.068 .
OriginTransplant:SiteSS 35.173 32.650 42.000 1.077 0.288
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.501
SiteSS -0.501 0.500
OrgnTrn:SSS 0.354 -0.707 -0.707eta_squared(Prot.S.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.01 | [0.00, 1.00]
Site | 0.25 | [0.08, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.S_TP2.res<-data.frame(summary(Prot.S.lme_TP2)$coefficients[-1,])
Prot.S_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP2.res$EtaSq<-c(eta_squared(Prot.S.lme_TP2)$Eta2)
Prot.S_TP2.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP2.res))
Effect size of Origin for each Site
##KL
Prot.S.lme_TP2_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Prot.S.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: 238.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.4406 -0.8006 0.2111 0.5424 1.7322
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 688.7 26.24
Residual 2134.1 46.20
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 329.068 20.185 3.247 16.303 0.000316 ***
OriginTransplant -29.475 18.860 20.000 -1.563 0.133770
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.467eta_squared(Prot.S.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.11 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP2_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Prot.S.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: 251.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.3034 -0.8616 0.0128 0.6244 2.1515
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1484 38.53
Residual 3840 61.96
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 372.313 28.545 3.078 13.043 0.000854 ***
OriginTransplant 5.698 25.297 20.000 0.225 0.824065
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.443eta_squared(Prot.S.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.53e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Prot.S_TP2.site.res<-data.frame(rbind(summary(Prot.S.lme_TP2_KL)$coefficients[-1,],
summary(Prot.S.lme_TP2_SS)$coefficients[-1,]))
Prot.S_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP2.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP2_KL)$Eta2, eta_squared(Prot.S.lme_TP2_SS)$Eta2)
Prot.S_TP2.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP2.site.res))
##Combine results
Prot.S_TP2.res<-rbind(Prot.S_TP2.res, Prot.S_TP2.site.res)
##Summary statistics by Site and Origin
TP2_ProtSym.sum<-summarySE(Coral.TP2, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP2_ProtSym.plot<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(250, 550); TP2_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_C)
shapiro.test(Coral.TP2$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP2$AFDW_mg.cm2_C
W = 0.98545, p-value = 0.8093#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP2<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Bio.C.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.C.lme_TP2, plot = F)
plot(Bio.C.lme_res_TP2)
##Model results
summary(Bio.C.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: -19.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.2645 -0.4732 -0.0846 0.5858 2.5448
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002429 0.04928
Residual 0.028719 0.16947
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.16312 0.05659 9.60683 20.552 2.91e-09 ***
OriginTransplant -0.10184 0.06918 42.00000 -1.472 0.1485
SiteSS -0.11111 0.06918 42.00000 -1.606 0.1158
OriginTransplant:SiteSS 0.26062 0.09784 42.00000 2.664 0.0109 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.611
SiteSS -0.611 0.500
OrgnTrn:SSS 0.432 -0.707 -0.707eta_squared(Bio.C.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 8.00e-03 | [0.00, 1.00]
Site | 3.65e-03 | [0.00, 1.00]
Origin:Site | 0.14 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.C_TP2.res<-data.frame(summary(Bio.C.lme_TP2)$coefficients[-1,])
Bio.C_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP2.res$EtaSq<-c(eta_squared(Bio.C.lme_TP2)$Eta2)
Bio.C_TP2.res$Response<-rep("Biomass Host", nrow(Bio.C_TP2.res))
Effect size of Origin for each Site
##KL
Bio.C.lme_TP2_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.C.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: -14.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.7100 -0.5598 -0.2566 0.3597 2.9272
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001355 0.03681
Residual 0.023645 0.15377
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.16312 0.04921 5.42756 23.634 1.11e-06 ***
OriginTransplant -0.10184 0.06278 20.00000 -1.622 0.12
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.638eta_squared(Bio.C.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.12 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP2_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Bio.C.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: -5.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.0676 -0.4964 0.1545 0.7280 1.2050
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03634 0.1906
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.05201 0.05503 22.00000 19.12 3.42e-15 ***
OriginTransplant 0.15878 0.07783 22.00000 2.04 0.0535 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.C.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.16 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.C_TP2.site.res<-data.frame(rbind(summary(Bio.C.lme_TP2_KL)$coefficients[-1,],
summary(Bio.C.lme_TP2_SS)$coefficients[-1,]))
Bio.C_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP2.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP2_KL)$Eta2, eta_squared(Bio.C.lme_TP2_SS)$Eta2)
Bio.C_TP2.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP2.site.res))
##Combine results
Bio.C_TP2.res<-rbind(Bio.C_TP2.res, Bio.C_TP2.site.res)
##Summary statistics by Site and Origin
TP2_BioHost.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP2_BioHost.plot<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.8, 1.6)+
annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold"); TP2_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_S)
shapiro.test(Coral.TP2$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP2$AFDW_mg.cm2_S
W = 0.98263, p-value = 0.6911#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP2<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Bio.S.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.S.lme_TP2, plot = F)
plot(Bio.S.lme_res_TP2)
##Model results
summary(Bio.S.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: -50.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.89274 -0.61267 -0.05248 0.62439 1.64962
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0003874 0.01968
Residual 0.0146825 0.12117
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.71562 0.03678 15.95638 19.457 1.54e-12 ***
OriginTransplant -0.12045 0.04947 42.00000 -2.435 0.0192 *
SiteSS 0.12854 0.04947 42.00000 2.599 0.0129 *
OriginTransplant:SiteSS 0.11020 0.06996 42.00000 1.575 0.1227
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.673
SiteSS -0.673 0.500
OrgnTrn:SSS 0.476 -0.707 -0.707eta_squared(Bio.S.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.08 | [0.00, 1.00]
Site | 0.40 | [0.21, 1.00]
Origin:Site | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.S_TP2.res<-data.frame(summary(Bio.S.lme_TP2)$coefficients[-1,])
Bio.S_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP2.res$EtaSq<-c(eta_squared(Bio.S.lme_TP2)$Eta2)
Bio.S_TP2.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP2.res))
Effect size of Origin for each Site
##KL
Bio.S.lme_TP2_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: -25.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.7872 -0.5807 -0.0580 0.6095 1.5698
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.01486 0.1219
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.71562 0.03519 22.00000 20.34 9.41e-16 ***
OriginTransplant -0.12045 0.04977 22.00000 -2.42 0.0242 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.21 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP2_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.S.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: -27.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.7098 -0.5388 -0.1840 0.6014 1.8524
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004978 0.07056
Residual 0.011447 0.10699
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.84416 0.05112 2.97784 16.513 0.000504 ***
OriginTransplant -0.01025 0.04368 20.00000 -0.235 0.816917
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.427eta_squared(Bio.S.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.74e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.S_TP2.site.res<-data.frame(rbind(summary(Bio.S.lme_TP2_KL)$coefficients[-1,],
summary(Bio.S.lme_TP2_SS)$coefficients[-1,]))
Bio.S_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP2.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP2_KL)$Eta2, eta_squared(Bio.S.lme_TP2_SS)$Eta2)
Bio.S_TP2.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP2.site.res))
##Combine results
Bio.S_TP2.res<-rbind(Bio.S_TP2.res, Bio.S_TP2.site.res)
##Summary statistics by Site and Origin
TP2_BioSym.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP2_BioSym.plot<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position= "top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.45, 0.95)+
annotate("text", x=1, y=0.78, label="*", size=sig.sz, fontface="bold"); TP2_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP2$Chl_ug.cm2)
shapiro.test(Coral.TP2$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP2$Chl_ug.cm2
W = 0.89322, p-value = 0.0003826#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP2<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP2)
##Check residuals
Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Chl.lme_TP2, plot = F)
plot(Chl.lme_res_TP2)
##Model results
summary(Chl.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin * Site + (1 | Genotype)
Data: Coral.TP2
REML criterion at convergence: 60.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.5646 -0.6834 -0.2260 0.6331 2.9227
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.02999 0.1732
Residual 0.17246 0.4153
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.2824 0.1561 6.2441 8.216 0.000142 ***
OriginTransplant -0.2475 0.1695 42.0000 -1.460 0.151795
SiteSS 0.5567 0.1695 42.0000 3.284 0.002071 **
OriginTransplant:SiteSS 0.6593 0.2398 42.0000 2.750 0.008761 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.543
SiteSS -0.543 0.500
OrgnTrn:SSS 0.384 -0.707 -0.707eta_squared(Chl.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.01 | [0.00, 1.00]
Site | 0.57 | [0.40, 1.00]
Origin:Site | 0.15 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Chl_TP2.res<-data.frame(summary(Chl.lme_TP2)$coefficients[-1,])
Chl_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP2.res$EtaSq<-c(eta_squared(Chl.lme_TP2)$Eta2)
Chl_TP2.res$Response<-rep("Chlorophyll", nrow(Chl_TP2.res))
Effect size of Origin for each Site
##KL
Chl.lme_TP2_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Chl.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "KL"), ]
REML criterion at convergence: 4.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.4118 -0.7633 -0.1784 0.6717 2.0082
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01088 0.1043
Residual 0.05353 0.2314
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.28244 0.08993 3.75836 14.26 0.000207 ***
OriginTransplant -0.24748 0.09445 20.00000 -2.62 0.016394 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.525eta_squared(Chl.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.26 | [0.03, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP2_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Chl.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl_ug.cm2 ~ Origin + (1 | Genotype)
Data: Coral.TP2[which(Coral.TP2$Site == "SS"), ]
REML criterion at convergence: 36.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.2515 -0.7498 -0.1994 0.6152 2.0640
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.1653 0.4065
Residual 0.2069 0.4548
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.8391 0.2689 2.5731 6.838 0.0103 *
OriginTransplant 0.4118 0.1857 20.0000 2.218 0.0383 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.345eta_squared(Chl.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.20 | [0.01, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Chl_TP2.site.res<-data.frame(rbind(summary(Chl.lme_TP2_KL)$coefficients[-1,],
summary(Chl.lme_TP2_SS)$coefficients[-1,]))
Chl_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP2.site.res$EtaSq<-c(eta_squared(Chl.lme_TP2_KL)$Eta2, eta_squared(Chl.lme_TP2_SS)$Eta2)
Chl_TP2.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP2.site.res))
##Combine results
Chl_TP2.res<-rbind(Chl_TP2.res, Chl_TP2.site.res)
##Summary statistics by Site and Origin
TP2_Chl.sum<-summarySE(Coral.TP2, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP2_Chl.plot<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position= "top")+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(0.5, 3.25)+
annotate("text", x=c(1,2), y=c(1.5, 2.6), label="*", size=sig.sz, fontface="bold"); TP2_Chl.plot
Save Results
##Combine Results
Phys_T2.res<-rbind(Prot.C_TP2.res, Prot.S_TP2.res, Bio.C_TP2.res, Bio.S_TP2.res, Chl_TP2.res)
##Add Timepoint
Phys_T2.res$TimeP<-rep("TP2", nrow(Phys_T2.res))
TP3
##Subset Timepoint 3
Coral.TP3<-subset(Coral.rm, TimeP=="TP3")
Protein Host
##Check normality
hist(Coral.TP3$TP_ug.cm2_C)
shapiro.test(Coral.TP3$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP3$TP_ug.cm2_C
W = 0.97395, p-value = 0.3582#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP3<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Prot.C.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.C.lme_TP3, plot = F)
plot(Prot.C.lme_res_TP3)
##Model results
summary(Prot.C.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 507.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.52281 -0.56107 -0.09932 0.69343 2.85970
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 1765 42.01
Residual 4332 65.82
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 354.577 30.812 3.885 11.508 0.000382 ***
OriginTransplant -28.338 26.869 42.000 -1.055 0.297608
SiteSS 11.510 26.869 42.000 0.428 0.670560
OriginTransplant:SiteSS 6.695 37.999 42.000 0.176 0.861001
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.436
SiteSS -0.436 0.500
OrgnTrn:SSS 0.308 -0.707 -0.707eta_squared(Prot.C.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 7.38e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.C_TP3.res<-data.frame(summary(Prot.C.lme_TP3)$coefficients[-1,])
Prot.C_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP3.res$EtaSq<-c(eta_squared(Prot.C.lme_TP3)$Eta2)
Prot.C_TP3.res$Response<-rep("Protein Host", nrow(Prot.C_TP3.res))
Protein Symbiont
##Check normality
hist(Coral.TP3$TP_ug.cm2_S)
shapiro.test(Coral.TP3$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP3$TP_ug.cm2_S
W = 0.95992, p-value = 0.1002#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP3<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Prot.S.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.S.lme_TP3, plot = F)
plot(Prot.S.lme_res_TP3)
##Model results
summary(Prot.S.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 497.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.04853 -0.63564 -0.04301 0.40723 2.41329
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 606.5 24.63
Residual 3588.9 59.91
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 350.037 22.388 6.353 15.635 2.61e-06 ***
OriginTransplant -31.070 24.457 42.000 -1.270 0.211
SiteSS 54.236 24.457 42.000 2.218 0.032 *
OriginTransplant:SiteSS 40.425 34.588 42.000 1.169 0.249
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.546
SiteSS -0.546 0.500
OrgnTrn:SSS 0.386 -0.707 -0.707eta_squared(Prot.S.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 9.30e-03 | [0.00, 1.00]
Site | 0.31 | [0.13, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.S_TP3.res<-data.frame(summary(Prot.S.lme_TP3)$coefficients[-1,])
Prot.S_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP3.res$EtaSq<-c(eta_squared(Prot.S.lme_TP3)$Eta2)
Prot.S_TP3.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP3.res))
Effect size of Origin for each Site
##KL
Prot.S.lme_TP3_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Prot.S.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: 240.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.73973 -0.61677 -0.06905 0.34373 2.28780
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 267.5 16.36
Residual 2444.8 49.45
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 350.037 17.114 4.572 20.453 1.14e-05 ***
OriginTransplant -31.070 20.186 20.000 -1.539 0.139
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.590eta_squared(Prot.S.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.11 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP3_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Prot.S.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: 255.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.80428 -0.53697 0.01298 0.48663 2.22260
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 939.7 30.66
Residual 4737.1 68.83
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 404.272 26.608 3.788 15.193 0.000156 ***
OriginTransplant 9.355 28.098 20.000 0.333 0.742643
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.528eta_squared(Prot.S.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 5.51e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Prot.S_TP3.site.res<-data.frame(rbind(summary(Prot.S.lme_TP3_KL)$coefficients[-1,],
summary(Prot.S.lme_TP3_SS)$coefficients[-1,]))
Prot.S_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP3.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP3_KL)$Eta2, eta_squared(Prot.S.lme_TP3_SS)$Eta2)
Prot.S_TP3.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP3.site.res))
##Combine results
Prot.S_TP3.res<-rbind(Prot.S_TP3.res, Prot.S_TP3.site.res)
##Summary statistics by Site and Origin
TP3_ProtSym.sum<-summarySE(Coral.TP3, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP3_ProtSym.plot<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(250, 550); TP3_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_C)
shapiro.test(Coral.TP3$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP3$AFDW_mg.cm2_C
W = 0.98042, p-value = 0.5962#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP3<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Bio.C.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.C.lme_TP3, plot = F)
plot(Bio.C.lme_res_TP3)
##Model results
summary(Bio.C.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: 1.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.39331 -0.54299 0.06699 0.61611 2.15929
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.007168 0.08467
Residual 0.045972 0.21441
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.18826 0.07887 6.67318 15.066 2.12e-06 ***
OriginTransplant -0.12291 0.08753 42.00000 -1.404 0.168
SiteSS 0.09539 0.08753 42.00000 1.090 0.282
OriginTransplant:SiteSS 0.12089 0.12379 42.00000 0.977 0.334
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.555
SiteSS -0.555 0.500
OrgnTrn:SSS 0.392 -0.707 -0.707eta_squared(Bio.C.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.13 | [0.01, 1.00]
Origin:Site | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.C_TP3.res<-data.frame(summary(Bio.C.lme_TP3)$coefficients[-1,])
Bio.C_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP3.res$EtaSq<-c(eta_squared(Bio.C.lme_TP3)$Eta2)
Bio.C_TP3.res$Response<-rep("Biomass Host", nrow(Bio.C_TP3.res))
Effect size of Origin for each Site
##KL
Bio.C.lme_TP3_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.C.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: 4.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.11835 -0.65919 -0.03638 0.66514 2.01105
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002671 0.05168
Residual 0.055878 0.23638
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.18826 0.07448 5.64458 15.955 6.53e-06 ***
OriginTransplant -0.12291 0.09650 20.00000 -1.274 0.217
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.648eta_squared(Bio.C.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.08 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP3_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.C.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -1.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.92899 -0.46522 0.05763 0.57468 2.06374
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006692 0.0818
Residual 0.039684 0.1992
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.283651 0.074415 3.992959 17.250 6.71e-05 ***
OriginTransplant -0.002024 0.081326 20.000004 -0.025 0.98
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.546eta_squared(Bio.C.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 3.10e-05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.C_TP3.site.res<-data.frame(rbind(summary(Bio.C.lme_TP3_KL)$coefficients[-1,],
summary(Bio.C.lme_TP3_SS)$coefficients[-1,]))
Bio.C_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP3.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP3_KL)$Eta2, eta_squared(Bio.C.lme_TP3_SS)$Eta2)
Bio.C_TP3.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP3.site.res))
##Combine results
Bio.C_TP3.res<-rbind(Bio.C_TP3.res, Bio.C_TP3.site.res)
##Summary statistics by Site and Origin
TP3_BioHost.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP3_BioHost.plot<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.8, 1.6); TP3_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_S)
shapiro.test(Coral.TP3$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP3$AFDW_mg.cm2_S
W = 0.95017, p-value = 0.04062#Slightly non Normal
hist(log(Coral.TP3$AFDW_mg.cm2_S+1))
shapiro.test(log(Coral.TP3$AFDW_mg.cm2_S+1))
Shapiro-Wilk normality test
data: log(Coral.TP3$AFDW_mg.cm2_S + 1)
W = 0.97082, p-value = 0.2726#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP3<-lmer(log(AFDW_mg.cm2_S+1)~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Bio.S.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.S.lme_TP3, plot = F)
plot(Bio.S.lme_res_TP3)
##Model results
summary(Bio.S.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: -59.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.9013 -0.6371 -0.1087 0.6759 2.0985
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001717 0.04143
Residual 0.011351 0.10654
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.50341 0.03896 6.79866 12.920 4.92e-06 ***
OriginTransplant -0.02185 0.04349 42.00000 -0.502 0.6180
SiteSS 0.07684 0.04349 42.00000 1.767 0.0845 .
OriginTransplant:SiteSS 0.04053 0.06151 42.00000 0.659 0.5136
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.558
SiteSS -0.558 0.500
OrgnTrn:SSS 0.395 -0.707 -0.707eta_squared(Bio.S.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.34e-05 | [0.00, 1.00]
Site | 0.19 | [0.04, 1.00]
Origin:Site | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.S_TP3.res<-data.frame(summary(Bio.S.lme_TP3)$coefficients[-1,])
Bio.S_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP3.res$EtaSq<-c(eta_squared(Bio.S.lme_TP3)$Eta2)
Bio.S_TP3.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP3.res))
Effect size of Origin for each Site
##KL
Bio.S.lme_TP3_KL<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.S.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: -32.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.19334 -0.63480 -0.09404 0.31252 2.01340
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001809 0.04253
Residual 0.009689 0.09843
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.50341 0.03755 3.86356 13.405 0.000221 ***
OriginTransplant -0.02185 0.04018 20.00000 -0.544 0.592623
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.535eta_squared(Bio.S.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP3_SS<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.S.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(AFDW_mg.cm2_S + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -26.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.70864 -0.59241 -0.00109 0.59598 2.08623
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0008163 0.02857
Residual 0.0136003 0.11662
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.58025 0.03749 5.37009 15.478 1.16e-05 ***
OriginTransplant 0.01868 0.04761 20.00000 0.392 0.699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.635eta_squared(Bio.S.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 7.63e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.S_TP3.site.res<-data.frame(rbind(summary(Bio.S.lme_TP3_KL)$coefficients[-1,],
summary(Bio.S.lme_TP3_SS)$coefficients[-1,]))
Bio.S_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP3.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP3_KL)$Eta2, eta_squared(Bio.S.lme_TP3_SS)$Eta2)
Bio.S_TP3.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP3.site.res))
##Combine results
Bio.S_TP3.res<-rbind(Bio.S_TP3.res, Bio.S_TP3.site.res)
##Summary statistics by Site and Origin
TP3_BioSym.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP3_BioSym.plot<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.45, 0.95); TP3_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP3$Chl_ug.cm2)
shapiro.test(Coral.TP3$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP3$Chl_ug.cm2
W = 0.92502, p-value = 0.004491#Not normal
hist(log(Coral.TP3$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP3$Chl_ug.cm2+1))
Shapiro-Wilk normality test
data: log(Coral.TP3$Chl_ug.cm2 + 1)
W = 0.97477, p-value = 0.3837#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP3<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP3)
##Check residuals
Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Chl.lme_TP3, plot = F)
plot(Chl.lme_res_TP3)
##Model results
summary(Chl.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP3
REML criterion at convergence: -35.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.25062 -0.55117 -0.02076 0.43420 2.75244
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01951 0.1397
Residual 0.01831 0.1353
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.81996 0.08960 2.71679 9.151 0.00406 **
OriginTransplant -0.10476 0.05525 42.00000 -1.896 0.06483 .
SiteSS 0.18045 0.05525 42.00000 3.266 0.00217 **
OriginTransplant:SiteSS 0.13664 0.07813 42.00000 1.749 0.08761 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.308
SiteSS -0.308 0.500
OrgnTrn:SSS 0.218 -0.707 -0.707eta_squared(Chl.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.49 | [0.31, 1.00]
Origin:Site | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Chl_TP3.res<-data.frame(summary(Chl.lme_TP3)$coefficients[-1,])
Chl_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP3.res$EtaSq<-c(eta_squared(Chl.lme_TP3)$Eta2)
Chl_TP3.res$Response<-rep("Chlorophyll", nrow(Chl_TP3.res))
Effect size of Origin for each Site
##KL
Chl.lme_TP3_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Chl.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "KL"), ]
REML criterion at convergence: -19
Scaled residuals:
Min 1Q Median 3Q Max
-1.7968 -0.6704 -0.2815 0.8139 2.1670
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009698 0.09848
Residual 0.016854 0.12982
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.8200 0.0681 2.7687 12.041 0.00179 **
OriginTransplant -0.1048 0.0530 20.0000 -1.977 0.06205 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.389eta_squared(Chl.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.16 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP3_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Chl.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP3[which(Coral.TP3$Site == "SS"), ]
REML criterion at convergence: -16.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.11066 -0.45318 -0.01187 0.63929 2.41481
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.03310 0.1819
Residual 0.01702 0.1305
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.00040 0.11159 2.24803 8.965 0.00837 **
OriginTransplant 0.03189 0.05326 20.00000 0.599 0.55610
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.239eta_squared(Chl.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Chl_TP3.site.res<-data.frame(rbind(summary(Chl.lme_TP3_KL)$coefficients[-1,],
summary(Chl.lme_TP3_SS)$coefficients[-1,]))
Chl_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP3.site.res$EtaSq<-c(eta_squared(Chl.lme_TP3_KL)$Eta2, eta_squared(Chl.lme_TP3_SS)$Eta2)
Chl_TP3.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP3.site.res))
##Combine results
Chl_TP3.res<-rbind(Chl_TP3.res, Chl_TP3.site.res)
##Summary statistics by Site and Origin
TP3_Chl.sum<-summarySE(Coral.TP3, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP3_Chl.plot<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(0.5, 3.25)+
annotate("text", x=1, y=1.5, label="-", size=levels.sz, fontface="bold"); TP3_Chl.plot
Save Results
##Combine Results
Phys_T3.res<-rbind(Prot.C_TP3.res, Prot.S_TP3.res, Bio.C_TP3.res, Bio.S_TP3.res, Chl_TP3.res)
##Add Timepoint
Phys_T3.res$TimeP<-rep("TP3", nrow(Phys_T3.res))
TP4
##Subset Timepoint 4
Coral.TP4<-subset(Coral.rm, TimeP=="TP4")
Protein Host
##Check normality
hist(Coral.TP4$TP_ug.cm2_C)
shapiro.test(Coral.TP4$TP_ug.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP4$TP_ug.cm2_C
W = 0.97592, p-value = 0.4503#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP4<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Prot.C.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.C.lme_TP4, plot = F)
plot(Prot.C.lme_res_TP4)
##Model results
summary(Prot.C.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 492.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.82648 -0.74846 0.02454 0.66400 1.93010
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 23.74 4.872
Residual 5739.32 75.758
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 329.20 22.05 20.43 14.930 1.86e-12 ***
OriginTransplant 21.29 31.63 40.27 0.673 0.505
SiteSS 50.44 30.93 40.12 1.631 0.111
OriginTransplant:SiteSS -67.31 44.72 40.12 -1.505 0.140
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.686
SiteSS -0.701 0.489
OrgnTrn:SSS 0.485 -0.707 -0.692eta_squared(Prot.C.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 7.51e-03 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 0.05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.C_TP4.res<-data.frame(summary(Prot.C.lme_TP4)$coefficients[-1,])
Prot.C_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP4.res$EtaSq<-c(eta_squared(Prot.C.lme_TP4)$Eta2)
Prot.C_TP4.res$Response<-rep("Protein Host", nrow(Prot.C_TP4.res))
Protein Symbiont
##Check normality
hist(Coral.TP4$TP_ug.cm2_S)
shapiro.test(Coral.TP4$TP_ug.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP4$TP_ug.cm2_S
W = 0.98874, p-value = 0.9316#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP4<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Prot.S.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.S.lme_TP4, plot = F)
plot(Prot.S.lme_res_TP4)
##Model results
summary(Prot.S.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 464
Scaled residuals:
Min 1Q Median 3Q Max
-1.9674 -0.6915 -0.1394 0.7468 1.8746
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 258.6 16.08
Residual 2793.9 52.86
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 283.146 17.862 8.966 15.852 7.28e-08 ***
OriginTransplant 2.715 22.078 40.118 0.123 0.903
SiteSS 94.191 21.579 40.048 4.365 8.71e-05 ***
OriginTransplant:SiteSS -14.298 31.203 40.048 -0.458 0.649
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.590
SiteSS -0.604 0.489
OrgnTrn:SSS 0.418 -0.707 -0.692eta_squared(Prot.S.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 2.00e-03 | [0.00, 1.00]
Site | 0.44 | [0.25, 1.00]
Origin:Site | 5.22e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Prot.S_TP4.res<-data.frame(summary(Prot.S.lme_TP4)$coefficients[-1,])
Prot.S_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP4.res$EtaSq<-c(eta_squared(Prot.S.lme_TP4)$Eta2)
Prot.S_TP4.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP4.res))
Effect size of Origin for each Site
##KL
Prot.S.lme_TP4_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Prot.S.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: 233.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.9026 -0.5130 -0.1508 0.5988 1.9630
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 123.5 11.11
Residual 3004.0 54.81
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 283.146 17.074 5.322 16.584 8.71e-06 ***
OriginTransplant 2.801 22.890 19.068 0.122 0.904
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.641eta_squared(Prot.S.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 7.84e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Prot.S.lme_TP4_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Prot.S.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: TP_ug.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: 231.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.95275 -0.71834 -0.03221 0.77696 1.62897
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 300.3 17.33
Residual 2653.9 51.52
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 377.337 17.924 4.502 21.05 1.14e-05 ***
OriginTransplant -10.979 21.525 19.180 -0.51 0.616
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.573eta_squared(Prot.S.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Prot.S_TP4.site.res<-data.frame(rbind(summary(Prot.S.lme_TP4_KL)$coefficients[-1,],
summary(Prot.S.lme_TP4_SS)$coefficients[-1,]))
Prot.S_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP4.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP4_KL)$Eta2, eta_squared(Prot.S.lme_TP4_SS)$Eta2)
Prot.S_TP4.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP4.site.res))
##Combine results
Prot.S_TP4.res<-rbind(Prot.S_TP4.res, Prot.S_TP4.site.res)
##Summary statistics by Site and Origin
TP4_ProtSym.sum<-summarySE(Coral.TP4, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Protein across Treatments
TP4_ProtSym.plot<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(250, 550); TP4_ProtSym.plot
Biomass Host
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_C)
shapiro.test(Coral.TP4$AFDW_mg.cm2_C)
Shapiro-Wilk normality test
data: Coral.TP4$AFDW_mg.cm2_C
W = 0.98656, p-value = 0.8669#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP4<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Bio.C.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.C.lme_TP4, plot = F)
plot(Bio.C.lme_res_TP4)
##Model results
summary(Bio.C.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: 31.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.94819 -0.65333 0.01107 0.59003 2.21357
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0006586 0.02566
Residual 0.0974518 0.31217
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.99501 0.09133 19.92205 10.895 7.67e-10 ***
OriginTransplant 0.17260 0.13032 40.29158 1.324 0.1928
SiteSS 0.33916 0.12744 40.15351 2.661 0.0111 *
OriginTransplant:SiteSS -0.38438 0.18428 40.15351 -2.086 0.0434 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.682
SiteSS -0.698 0.489
OrgnTrn:SSS 0.483 -0.707 -0.692eta_squared(Bio.C.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 1.12e-03 | [0.00, 1.00]
Site | 0.06 | [0.00, 1.00]
Origin:Site | 0.10 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.C_TP4.res<-data.frame(summary(Bio.C.lme_TP4)$coefficients[-1,])
Bio.C_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP4.res$EtaSq<-c(eta_squared(Bio.C.lme_TP4)$Eta2)
Bio.C_TP4.res$Response<-rep("Biomass Host", nrow(Bio.C_TP4.res))
Effect size of Origin for each Site
##KL
Bio.C.lme_TP4_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.C.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: 22.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.65092 -0.56347 0.01929 0.47522 1.89563
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0000 0.0000
Residual 0.1361 0.3689
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.9950 0.1065 21.0000 9.344 6.27e-09 ***
OriginTransplant 0.1727 0.1540 21.0000 1.121 0.275
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.C.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.C.lme_TP4_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.C.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_C ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: 4.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.2767 -0.7007 0.1653 0.5506 2.0180
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009792 0.09895
Residual 0.052524 0.22918
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.33417 0.08741 3.82984 15.263 0.000143 ***
OriginTransplant -0.20931 0.09578 19.12799 -2.185 0.041513 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.523eta_squared(Bio.C.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.20 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.C_TP4.site.res<-data.frame(rbind(summary(Bio.C.lme_TP4_KL)$coefficients[-1,],
summary(Bio.C.lme_TP4_SS)$coefficients[-1,]))
Bio.C_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP4.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP4_KL)$Eta2, eta_squared(Bio.C.lme_TP4_SS)$Eta2)
Bio.C_TP4.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP4.site.res))
##Combine results
Bio.C_TP4.res<-rbind(Bio.C_TP4.res, Bio.C_TP4.site.res)
##Summary statistics by Site and Origin
TP4_BioHost.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Host Biomass across Treatments
TP4_BioHost.plot<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.8, 1.6)+
annotate("text", x=2, y=1.45, label="*", size=sig.sz, fontface="bold"); TP4_BioHost.plot
Biomass Symbionts
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_S)
shapiro.test(Coral.TP4$AFDW_mg.cm2_S)
Shapiro-Wilk normality test
data: Coral.TP4$AFDW_mg.cm2_S
W = 0.96144, p-value = 0.1304#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP4<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)boundary (singular) fit: see help('isSingular')##Check residuals
Bio.S.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.S.lme_TP4, plot = F)
plot(Bio.S.lme_res_TP4)
##Model results
summary(Bio.S.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: -13.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.9850 -0.7648 -0.1116 0.6976 1.8287
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03381 0.1839
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.58851 0.05308 42.00000 11.087 4.71e-14 ***
OriginTransplant 0.02029 0.07675 42.00000 0.264 0.7928
SiteSS 0.15893 0.07507 42.00000 2.117 0.0402 *
OriginTransplant:SiteSS -0.05653 0.10855 42.00000 -0.521 0.6052
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.692
SiteSS -0.707 0.489
OrgnTrn:SSS 0.489 -0.707 -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 5.14e-04 | [0.00, 1.00]
Site | 0.12 | [0.01, 1.00]
Origin:Site | 6.42e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Bio.S_TP4.res<-data.frame(summary(Bio.S.lme_TP4)$coefficients[-1,])
Bio.S_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP4.res$EtaSq<-c(eta_squared(Bio.S.lme_TP4)$Eta2)
Bio.S_TP4.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP4.res))
Effect size of Origin for each Site
##KL
Bio.S.lme_TP4_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: -9
Scaled residuals:
Min 1Q Median 3Q Max
-1.8536 -0.7454 -0.2379 0.7005 1.9338
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03023 0.1739
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.58851 0.05019 21.00000 11.72 1.12e-10 ***
OriginTransplant 0.02029 0.07258 21.00000 0.28 0.783
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 3.71e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Bio.S.lme_TP4_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Bio.S.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: AFDW_mg.cm2_S ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: -4.5
Scaled residuals:
Min 1Q Median 3Q Max
-1.8877 -0.8214 0.1483 0.7055 1.5821
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.03739 0.1934
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.74744 0.05582 21.00000 13.391 9.4e-12 ***
OriginTransplant -0.03625 0.08071 21.00000 -0.449 0.658
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.692
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Bio.S.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 9.51e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Bio.S_TP4.site.res<-data.frame(rbind(summary(Bio.S.lme_TP4_KL)$coefficients[-1,],
summary(Bio.S.lme_TP4_SS)$coefficients[-1,]))
Bio.S_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP4.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP4_KL)$Eta2, eta_squared(Bio.S.lme_TP4_SS)$Eta2)
Bio.S_TP4.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP4.site.res))
##Combine results
Bio.S_TP4.res<-rbind(Bio.S_TP4.res, Bio.S_TP4.site.res)
##Summary statistics by Site and Origin
TP4_BioSym.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Biomass across Treatments
TP4_BioSym.plot<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
ylim(0.45, 0.95); TP4_BioSym.plot
Chlorophyll
##Check normality
hist(Coral.TP4$Chl_ug.cm2)
shapiro.test(Coral.TP4$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Coral.TP4$Chl_ug.cm2
W = 0.94799, p-value = 0.03927#Slightly non normal
hist(log(Coral.TP4$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP4$Chl_ug.cm2+1))
Shapiro-Wilk normality test
data: log(Coral.TP4$Chl_ug.cm2 + 1)
W = 0.97457, p-value = 0.4047#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP4<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP4)
##Check residuals
Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Chl.lme_TP4, plot = F)
plot(Chl.lme_res_TP4)
##Model results
summary(Chl.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin * Site + (1 | Genotype)
Data: Coral.TP4
REML criterion at convergence: -35.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.5527 -0.6247 0.1083 0.5659 1.8596
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.02813 0.1677
Residual 0.01693 0.1301
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.939828 0.103867 2.456396 9.048 0.00604 **
OriginTransplant -0.005543 0.054370 40.013465 -0.102 0.91931
SiteSS 0.335684 0.053119 40.006608 6.319 1.68e-07 ***
OriginTransplant:SiteSS 0.049977 0.076810 40.006608 0.651 0.51899
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.250
SiteSS -0.256 0.489
OrgnTrn:SSS 0.177 -0.706 -0.692eta_squared(Chl.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.34e-03 | [0.00, 1.00]
Site | 0.69 | [0.55, 1.00]
Origin:Site | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save model results
Chl_TP4.res<-data.frame(summary(Chl.lme_TP4)$coefficients[-1,])
Chl_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP4.res$EtaSq<-c(eta_squared(Chl.lme_TP4)$Eta2)
Chl_TP4.res$Response<-rep("Chlorophyll", nrow(Chl_TP4.res))
Effect size of Origin for each Site
##KL
Chl.lme_TP4_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Chl.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "KL"), ]
REML criterion at convergence: -14
Scaled residuals:
Min 1Q Median 3Q Max
-1.59344 -0.58861 -0.05038 0.48137 1.88220
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01194 0.1093
Residual 0.02028 0.1424
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.939828 0.075300 2.739672 12.481 0.00171 **
OriginTransplant -0.005243 0.059554 19.055343 -0.088 0.93076
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.377eta_squared(Chl.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 4.07e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Chl.lme_TP4_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Chl.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Origin + (1 | Genotype)
Data: Coral.TP4[which(Coral.TP4$Site == "SS"), ]
REML criterion at convergence: -25
Scaled residuals:
Min 1Q Median 3Q Max
-2.80744 -0.47238 0.03677 0.58308 1.31155
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.049098 0.22158
Residual 0.009964 0.09982
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.27551 0.13113 2.09990 9.727 0.00885 **
OriginTransplant 0.04346 0.04175 19.00925 1.041 0.31096
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.152eta_squared(Chl.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Save results
Chl_TP4.site.res<-data.frame(rbind(summary(Chl.lme_TP4_KL)$coefficients[-1,],
summary(Chl.lme_TP4_SS)$coefficients[-1,]))
Chl_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP4.site.res$EtaSq<-c(eta_squared(Chl.lme_TP4_KL)$Eta2, eta_squared(Chl.lme_TP4_SS)$Eta2)
Chl_TP4.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP4.site.res))
##Combine results
Chl_TP4.res<-rbind(Chl_TP4.res, Chl_TP4.site.res)
##Summary statistics by Site and Origin
TP4_Chl.sum<-summarySE(Coral.TP4, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
TP4_Chl.plot<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position="top")+
labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
ylim(0.5, 3.25); TP4_Chl.plot
Save Results
##Combine Results
Phys_T4.res<-rbind(Prot.C_TP4.res, Prot.S_TP4.res, Bio.C_TP4.res, Bio.S_TP4.res, Chl_TP4.res)
##Add Timepoint
Phys_T4.res$TimeP<-rep("TP4", nrow(Phys_T4.res))
Effect Size over Time
##Dataframe of effect size results
Phys.ES<-rbind(Phys_T1.res, Phys_T2.res, Phys_T3.res, Phys_T4.res)
Phys.ES<-Phys.ES %>% dplyr::rename(p = Pr...t..)
Phys.ES<-Phys.ES[,c("TimeP", "Predictor", "Response", "EtaSq", "p")]
##Site specific results
Phys.ES<-Phys.ES[which(Phys.ES$Predictor=="KL Origin" | Phys.ES$Predictor=="SS Origin"),]
Phys.ES<-Phys.ES %>% separate_wider_delim(cols="Predictor", delim=" ", names=c("Site", "Predictor"), cols_remove = TRUE)
##Add Metric names
Phys.ES$Metric<-ifelse(Phys.ES$Response== "Protein Host", "TP_ug.cm2_C", ifelse(
Phys.ES$Response== "Protein Symbiont", "TP_ug.cm2_S", ifelse(
Phys.ES$Response=="Biomass Host", "AFDW_mg.cm2_C", ifelse(
Phys.ES$Response=="Biomass Symbiont", "AFDW_mg.cm2_S", ifelse(
Phys.ES$Response=="Chlorophyll", "Chl_ug.cm2", NA)))))
##Add Significance levels
Phys.ES$Sig<-ifelse(Phys.ES$p<0.001, "***", ifelse(Phys.ES$p<0.01, "**",
ifelse(Phys.ES$p<0.05, "*", ifelse(Phys.ES$p<0.1, "-", NA))))
Host Biomass
Bio.C.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Host Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5),
axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position=c(.9, .8))+
labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.C.ES.plot
Symbiont Biomass
Bio.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Biomass")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5),
axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position=c(.9, .8))+
labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.S.ES.plot
Symbiont Protein
Prot.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Protein")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5),
axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position=c(.9, .8))+
labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Prot.S.ES.plot
Chlorophyll
Chl.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Chlorophyll")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5),
axis.title.x = element_text(size = axis.title.sz),
axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"),
axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz),
legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"),
legend.position=c(.9, .8))+
labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.6)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Chl.ES.plot
Figures
Figure 3 Univariate Physiology
Adjust for Panel
##Biomass Host
TP1_BioHost.plot<-TP1_BioHost.plot+ ggtitle("TP1")+ labs(x="")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
legend.position=c(0.5, 0.85), legend.direction="horizontal")+
guides(color=guide_legend(nrow=2, byrow=FALSE))
TP2_BioHost.plot<-TP2_BioHost.plot+ ggtitle("TP2")+ labs(x="", y="") +
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
legend.position="none")
TP3_BioHost.plot<-TP3_BioHost.plot+ ggtitle("TP3")+ labs(x="", y="") +
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
legend.position="none")
TP4_BioHost.plot<-TP4_BioHost.plot+ ggtitle("TP4")+ labs(x="", y="") +
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),
legend.position="none")
Bio.C.ES.plot<-Bio.C.ES.plot + theme(legend.position=c(.15, .8))
##Biomass Symbiont
TP1_BioSym.plot<-TP1_BioSym.plot + labs(x="") + theme(legend.position="none")
TP2_BioSym.plot<-TP2_BioSym.plot + labs(x="", y="") + theme(legend.position="none")
TP3_BioSym.plot<-TP3_BioSym.plot + labs(x="", y="") + theme(legend.position="none")
TP4_BioSym.plot<-TP4_BioSym.plot + labs(x="", y="") + theme(legend.position="none")
Bio.S.ES.plot<-Bio.S.ES.plot + theme(legend.position="none")
##Protein Symbiont
TP1_ProtSym.plot<-TP1_ProtSym.plot + labs(x="") + theme(legend.position="none")
TP2_ProtSym.plot<-TP2_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")
TP3_ProtSym.plot<-TP3_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")
TP4_ProtSym.plot<-TP4_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")
Prot.S.ES.plot<-Prot.S.ES.plot + theme(legend.position="none")
##Chlorophyll
TP1_Chl.plot<-TP1_Chl.plot + theme(legend.position="none")
TP2_Chl.plot<-TP2_Chl.plot + labs(y="") + theme(legend.position="none")
TP3_Chl.plot<-TP3_Chl.plot + labs(y="") + theme(legend.position="none")
TP4_Chl.plot<-TP4_Chl.plot + labs(y="") + theme(legend.position="none")
Chl.ES.plot<-Chl.ES.plot + theme(legend.position="none")Figure 3
##Create Panel
Phys_fig<-plot_grid(TP1_BioHost.plot, TP2_BioHost.plot,
TP3_BioHost.plot, TP4_BioHost.plot, Bio.C.ES.plot,
TP1_BioSym.plot, TP2_BioSym.plot,
TP3_BioSym.plot, TP4_BioSym.plot, Bio.S.ES.plot,
TP1_ProtSym.plot, TP2_ProtSym.plot,
TP3_ProtSym.plot, TP4_ProtSym.plot, Prot.S.ES.plot,
TP1_Chl.plot, TP2_Chl.plot,
TP3_Chl.plot, TP4_Chl.plot, Chl.ES.plot,
rel_widths=c(0.8, 0.8, 0.8, 0.8, 1), rel_heights = 1,
nrow=4, ncol=5, byrow=T, labels = NULL)Warning: Removed 5 rows containing missing values (`geom_text()`).Warning: Removed 6 rows containing missing values (`geom_text()`).Warning: Removed 7 rows containing missing values (`geom_text()`).Warning: Removed 4 rows containing missing values (`geom_text()`).##Save Figure
ggsave(filename="Figures/02_Physiology/Fig3_Physiology.png", plot=Phys_fig, dpi=300, width=16, height=16, units="in")
Tables
Table S3A Physiology PERMANOVA and PERMDISP
##Combine Results Tables
TableS3A_Phys.PERM<-data.frame(rbind(PERM_T1.res, PERM_T2.res, PERM_T3.res, PERM_T4.res))
##Organize
names(TableS3A_Phys.PERM)[1] "Df" "SumOfSqs" "F" "parOmegaSq" "Pr..F." "Predictor" "p_DISP"
[8] "Timepoint" TableS3A_Phys.PERM<-TableS3A_Phys.PERM %>% dplyr::rename( DF = Df, SS = SumOfSqs, EffectSize = parOmegaSq, p = Pr..F.)
TableS3A_Phys.PERM<-TableS3A_Phys.PERM[,c("Timepoint", "Predictor", "DF", "SS", "F", "EffectSize", "p", "p_DISP")]
#Round to 3 digits
TableS3A_Phys.PERM$SS<-round(TableS3A_Phys.PERM$SS, 3)
TableS3A_Phys.PERM$F<-round(TableS3A_Phys.PERM$F, 3)
TableS3A_Phys.PERM$EffectSize<-round(TableS3A_Phys.PERM$EffectSize, 3)
TableS3A_Phys.PERM$p<-round(TableS3A_Phys.PERM$p, 3)
TableS3A_Phys.PERM$p_DISP<-round(TableS3A_Phys.PERM$p_DISP, 3)
##Write Out Table
write.csv(TableS3A_Phys.PERM, "Tables/TableS3A_Physiology_PERM_Results.csv", row.names=FALSE)
Table S4 Physiology Univariate LM
##Combine Results Tables
TableS4_Phys.LM<-data.frame(rbind(Phys_T1.res, Phys_T2.res, Phys_T3.res, Phys_T4.res))
##Organize
names(TableS4_Phys.LM)[1] "Estimate" "Std..Error" "df" "t.value" "Pr...t.." "Predictor" "EtaSq"
[8] "Response" "TimeP" TableS4_Phys.LM<-TableS4_Phys.LM %>% dplyr::rename(SE = Std..Error, DF = df, t = t.value, p = Pr...t.., EffectSize = EtaSq, Timepoint = TimeP)
TableS4_Phys.LM<-TableS4_Phys.LM[,c("Timepoint", "Response", "Predictor", "Estimate", "SE", "DF", "t", "p", "EffectSize")]
#Round to 3 digits
TableS4_Phys.LM$Estimate<-round(TableS4_Phys.LM$Estimate, 3)
TableS4_Phys.LM$SE<-round(TableS4_Phys.LM$SE, 3)
TableS4_Phys.LM$t<-round(TableS4_Phys.LM$t, 3)
TableS4_Phys.LM$p<-round(TableS4_Phys.LM$p, 3)
TableS4_Phys.LM$EffectSize<-round(TableS4_Phys.LM$EffectSize, 3)
#Integer
TableS4_Phys.LM$DF<-round(TableS4_Phys.LM$DF, 0)
##Write Out Table
write.csv(TableS4_Phys.LM, "Tables/TableS4_Physiology_LM_Results.csv", row.names=FALSE)
---
title: "Coral Physiology Following Reciprocal Transplant"
author: "Serena Hackerott"
date: "1/1/2024"
output:
  html_notebook:
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
    df_print: paged
---

# Setup

```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```


### Load Packages
```{r}
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("lme4")) install.packages("lme4")
if (!require("vegan")) install.packages("vegan")
if (!require("corrplot")) install.packages("corrplot")
if (!require("DHARMa")) install.packages("DHARMa")
if (!require("effectsize")) install.packages("effectsize")
if (!require("emmeans")) install.packages("emmeans")
if (!require("Hmisc")) install.packages("Hmisc")
if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyr")) install.packages("tidyr")

##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(Rmisc) #Required for SummarySE function
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(corrplot) #Required for correlation plot
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons 
library(Hmisc) #Required for correlations
library(dplyr) #Required for dataframe organization
library(tidyr) #Required for dataframe organization

```


### Graphing Parameters
```{r}
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd file
```


# Sample Data and Metadata

### Load and Organize Data
```{r}
##Load Data
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Coral<-read.csv("Outputs/CoralData.csv", header=TRUE)

##Set factor variables
Coral$TimeP<-factor(Coral$TimeP, levels=c("TP1", "TP2", "TP3", "TP4"))
Coral$Site<-factor(Coral$Site, levels=c("KL", "SS"))
Coral$Genotype<-factor(Coral$Genotype, levels=c("AC8", "AC10", "AC12"))
Coral$Orig<-factor(Coral$Orig, levels=c("N", "T"))
Coral$Origin<-factor(Coral$Origin, levels=c("Native", "Transplant"))
Coral$Site.Orig<-factor(Coral$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))


```


# Multivariate Analysis

### adonis OmegaSq Function
```{r}
#' Calculate (partial) Omega-squared (effect-size calculation) for PERMANOVA and add it to the input object
#'
#' @param adonisOutput An adonis object
#' @param partial Should partial omega-squared be calculated (sample size adjusted). Default TRUE
#' @return Original adonis object with the (partial) Omega-squared values added
#' @import vegan
#' @export
adonis_OmegaSq <- function(adonisOutput, partial = TRUE){
    if(!(is(adonisOutput, "adonis") || is(adonisOutput, "anova.cca")))
        stop("Input should be an adonis object")
    if (is(adonisOutput, "anova.cca")) {
        aov_tab <- adonisOutput
        aov_tab$MeanSqs <- aov_tab$SumOfSqs / aov_tab$Df
        aov_tab$MeanSqs[length(aov_tab$Df)] <- NA
    } else {
        aov_tab <- adonisOutput$aov.tab
    }
    heading <- attr(aov_tab, "heading")
    MS_res <- aov_tab[pmatch("Residual", rownames(aov_tab)), "MeanSqs"]
    SS_tot <- aov_tab[rownames(aov_tab) == "Total", "SumsOfSqs"]
    N <- aov_tab[rownames(aov_tab) == "Total", "Df"] + 1
    if(partial){
        omega <- apply(aov_tab, 1, function(x) (x["Df"]*(x["MeanSqs"]-MS_res))/(x["Df"]*x["MeanSqs"]+(N-x["Df"])*MS_res))
        aov_tab$parOmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
    } else {
        omega <- apply(aov_tab, 1, function(x) (x["SumsOfSqs"]-x["Df"]*MS_res)/(SS_tot+MS_res))
        aov_tab$OmegaSq <- c(omega[1:(length(omega)-2)], NA, NA)
    }
    if (is(adonisOutput, "adonis"))
        cn_order <- c("Df", "SumsOfSqs", "MeanSqs", "F.Model", "R2",
                      if (partial) "parOmegaSq" else "OmegaSq", "Pr(>F)")
    else
        cn_order <- c("Df", "SumOfSqs", "F", if (partial) "parOmegaSq" else "OmegaSq",
                      "Pr(>F)")
    aov_tab <- aov_tab[, cn_order]
    attr(aov_tab, "names") <- cn_order
    attr(aov_tab, "heading") <- heading
    if (is(adonisOutput, "adonis"))
        adonisOutput$aov.tab <- aov_tab
    else
        adonisOutput <- aov_tab
    return(adonisOutput)
}
```


### Check Correlation
```{r}
##Remove NA's
names(Coral)
Coral.rm<-na.omit(Coral)

##Log +1 transform 
Coral.log<-Coral.rm
Coral.log[,-c(1:10)]<-log(Coral.rm[,-c(1:10)]+1)
  
Phys.cor<-rcorr(as.matrix(Coral.log[,-c(1:10)]), type="pearson")
Phys.cor

diag(Phys.cor$P)<-0

corrplot(Phys.cor$r, type="upper", order="hclust", 
         p.mat = Phys.cor$P, sig.level = 0.01, insig = "blank")

```


### TP1
```{r}
##Subset Timepoint 1 
Coral.log_TP1<-subset(Coral.log, TimeP=="TP1")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP1.perm<-adonis2(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin * Coral.log_TP1$Site, data=Coral.log_TP1, strata=Coral.log_TP1$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm)

##Check dispersion by Origin
TP1.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin))
TP1.disp_orig

##Check dispersion by Site
TP1.disp_site<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Site))
TP1.disp_site

##Check dispersion by Origin:Site
TP1.disp_os<-anova(betadisper(vegdist(Coral.log_TP1[,c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin:Coral.log_TP1$Site))
TP1.disp_os

##Save results
PERM_T1.res<-data.frame(adonis_OmegaSq(Coral.TP1.perm)[1:3,])
PERM_T1.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T1.res$p_DISP<-c(TP1.disp_orig$`Pr(>F)`[1], TP1.disp_site$`Pr(>F)`[1], TP1.disp_os$`Pr(>F)`[1])
```
Physiology differs significantly by Origin and the effect of Origin differs between Sites.

#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP1.perm_KL<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm_KL)

##Check dispersion by Origin
TP1.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="KL")]))
TP1.KL.disp_orig

##SS
##PERMANOVA
Coral.TP1.perm_SS<-adonis2(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")], data=Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),], strata=Coral.log_TP1$Genotype[which(Coral.log_TP1$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP1.perm_SS)

##Check dispersion by Origin
TP1.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP1[which(Coral.log_TP1$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP1$Origin[which(Coral.log_TP1$Site=="SS")]))
TP1.SS.disp_orig

##Save results
PERM_T1.site.res<-data.frame(adonis_OmegaSq(Coral.TP1.perm_KL)[1,])
PERM_T1.site.res<-rbind(PERM_T1.site.res, data.frame(adonis_OmegaSq(Coral.TP1.perm_SS)[1,]))
PERM_T1.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T1.site.res$p_DISP<-c(TP1.KL.disp_orig$`Pr(>F)`[1], TP1.SS.disp_orig$`Pr(>F)`[1])

##Combine results 
PERM_T1.res<-rbind(PERM_T1.res, PERM_T1.site.res)
PERM_T1.res$Timepoint<-rep("TP1", nrow(PERM_T1.res))

```

### TP2
```{r}
##Subset Timepoint 2 
Coral.log_TP2<-subset(Coral.log, TimeP=="TP2")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP2.perm<-adonis2(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin * Coral.log_TP2$Site, data=Coral.log_TP2, strata=Coral.log_TP2$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm)

##Check dispersion by Origin
TP2.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin))
TP2.disp_orig

##Check dispersion by Site
TP2.disp_site<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Site))
TP2.disp_site

##Check dispersion by Origin:Site
TP2.disp_os<-anova(betadisper(vegdist(Coral.log_TP2[,c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin:Coral.log_TP2$Site))
TP2.disp_os

##Save results
PERM_T2.res<-data.frame(adonis_OmegaSq(Coral.TP2.perm)[1:3,])
PERM_T2.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T2.res$p_DISP<-c(TP2.disp_orig$`Pr(>F)`[1], TP2.disp_site$`Pr(>F)`[1], TP2.disp_os$`Pr(>F)`[1])

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP2.perm_KL<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm_KL)

##Check dispersion by Origin
TP2.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="KL")]))
TP2.KL.disp_orig

##SS
##PERMANOVA
Coral.TP2.perm_SS<-adonis2(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")], data=Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),], strata=Coral.log_TP2$Genotype[which(Coral.log_TP2$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP2.perm_SS)

##Check dispersion by Origin
TP2.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP2[which(Coral.log_TP2$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP2$Origin[which(Coral.log_TP2$Site=="SS")]))
TP2.SS.disp_orig

##Save results
PERM_T2.site.res<-data.frame(adonis_OmegaSq(Coral.TP2.perm_KL)[1,])
PERM_T2.site.res<-rbind(PERM_T2.site.res, data.frame(adonis_OmegaSq(Coral.TP2.perm_SS)[1,]))
PERM_T2.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T2.site.res$p_DISP<-c(TP2.KL.disp_orig$`Pr(>F)`[1], TP2.SS.disp_orig$`Pr(>F)`[1])

##Combine results 
PERM_T2.res<-rbind(PERM_T2.res, PERM_T2.site.res)
PERM_T2.res$Timepoint<-rep("TP2", nrow(PERM_T2.res))

```


### TP3
```{r}
##Subset Timepoint 3 
Coral.log_TP3<-subset(Coral.log, TimeP=="TP3")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP3.perm<-adonis2(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin * Coral.log_TP3$Site, data=Coral.log_TP3, strata=Coral.log_TP3$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm)

##Check dispersion by Origin
TP3.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin))
TP3.disp_orig

##Check dispersion by Site
TP3.disp_site<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Site))
TP3.disp_site

##Check dispersion by Origin:Site
TP3.disp_os<-anova(betadisper(vegdist(Coral.log_TP3[,c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin:Coral.log_TP3$Site))
TP3.disp_os

##Save results
PERM_T3.res<-data.frame(adonis_OmegaSq(Coral.TP3.perm)[1:3,])
PERM_T3.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T3.res$p_DISP<-c(TP3.disp_orig$`Pr(>F)`[1], TP3.disp_site$`Pr(>F)`[1], TP3.disp_os$`Pr(>F)`[1])

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP3.perm_KL<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm_KL)

##Check dispersion by Origin
TP3.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="KL")]))
TP3.KL.disp_orig

##SS
##PERMANOVA
Coral.TP3.perm_SS<-adonis2(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")], data=Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),], strata=Coral.log_TP3$Genotype[which(Coral.log_TP3$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP3.perm_SS)

##Check dispersion by Origin
TP3.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP3[which(Coral.log_TP3$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP3$Origin[which(Coral.log_TP3$Site=="SS")]))
TP3.SS.disp_orig

##Save results
PERM_T3.site.res<-data.frame(adonis_OmegaSq(Coral.TP3.perm_KL)[1,])
PERM_T3.site.res<-rbind(PERM_T3.site.res, data.frame(adonis_OmegaSq(Coral.TP3.perm_SS)[1,]))
PERM_T3.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T3.site.res$p_DISP<-c(TP3.KL.disp_orig$`Pr(>F)`[1], TP3.SS.disp_orig$`Pr(>F)`[1])

##Combine results 
PERM_T3.res<-rbind(PERM_T3.res, PERM_T3.site.res)
PERM_T3.res$Timepoint<-rep("TP3", nrow(PERM_T3.res))


```


### TP4
```{r}
##Subset Timepoint 4 
Coral.log_TP4<-subset(Coral.log, TimeP=="TP4")

```

#### PERMANOVA
```{r}
##PERMANOVA
Coral.TP4.perm<-adonis2(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin * Coral.log_TP4$Site, data=Coral.log_TP4, strata=Coral.log_TP4$Genotype, method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm)

##Check dispersion by Origin
TP4.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin))
TP4.disp_orig

##Check dispersion by Site
TP4.disp_site<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Site))
TP4.disp_site

##Check dispersion by Origin:Site
TP4.disp_os<-anova(betadisper(vegdist(Coral.log_TP4[,c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin:Coral.log_TP4$Site))
TP4.disp_os

##Save results
PERM_T4.res<-data.frame(adonis_OmegaSq(Coral.TP4.perm)[1:3,])
PERM_T4.res$Predictor<-c("Origin", "Site", "Origin x Site")
PERM_T4.res$p_DISP<-c(TP4.disp_orig$`Pr(>F)`[1], TP4.disp_site$`Pr(>F)`[1], TP4.disp_os$`Pr(>F)`[1])

```


#### Effect Size
Calculate Effect Size of Origin for each Site
```{r}
##KL
##PERMANOVA
Coral.TP4.perm_KL<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="KL")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm_KL)

##Check dispersion by Origin
TP4.KL.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="KL"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="KL")]))
TP4.KL.disp_orig

##SS
##PERMANOVA
Coral.TP4.perm_SS<-adonis2(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean")~ Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")], data=Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),], strata=Coral.log_TP4$Genotype[which(Coral.log_TP4$Site=="SS")], method="euclidean")

##Effect Size
adonis_OmegaSq(Coral.TP4.perm_SS)

##Check dispersion by Origin
TP4.SS.disp_orig<-anova(betadisper(vegdist(Coral.log_TP4[which(Coral.log_TP4$Site=="SS"),c(11:14, 16)], "euclidean"), Coral.log_TP4$Origin[which(Coral.log_TP4$Site=="SS")]))
TP4.SS.disp_orig

##Save results
PERM_T4.site.res<-data.frame(adonis_OmegaSq(Coral.TP4.perm_KL)[1,])
PERM_T4.site.res<-rbind(PERM_T4.site.res, data.frame(adonis_OmegaSq(Coral.TP4.perm_SS)[1,]))
PERM_T4.site.res$Predictor<-c("KL Origin", "SS Origin")
PERM_T4.site.res$p_DISP<-c(TP4.KL.disp_orig$`Pr(>F)`[1], TP4.SS.disp_orig$`Pr(>F)`[1])

##Combine results 
PERM_T4.res<-rbind(PERM_T4.res, PERM_T4.site.res)
PERM_T4.res$Timepoint<-rep("TP4", nrow(PERM_T4.res))

```


# Univariate 

### TP1

```{r}
##Subset Timepoint 1 
Coral.TP1<-subset(Coral.rm, TimeP=="TP1")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP1$TP_ug.cm2_C)
shapiro.test(Coral.TP1$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP1<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Prot.C.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.C.lme_TP1, plot = F)
plot(Prot.C.lme_res_TP1)

##Model results
summary(Prot.C.lme_TP1)

eta_squared(Prot.C.lme_TP1)

##Save model results
Prot.C_TP1.res<-data.frame(summary(Prot.C.lme_TP1)$coefficients[-1,])
Prot.C_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP1.res$EtaSq<-c(eta_squared(Prot.C.lme_TP1)$Eta2)
Prot.C_TP1.res$Response<-rep("Protein Host", nrow(Prot.C_TP1.res))
```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP1$TP_ug.cm2_S)
shapiro.test(Coral.TP1$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP1<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Prot.S.lme_res_TP1 <- simulateResiduals(fittedModel = Prot.S.lme_TP1, plot = F)
plot(Prot.S.lme_res_TP1)

##Model results
summary(Prot.S.lme_TP1)

eta_squared(Prot.S.lme_TP1)

##Save model results
Prot.S_TP1.res<-data.frame(summary(Prot.S.lme_TP1)$coefficients[-1,])
Prot.S_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP1.res$EtaSq<-c(eta_squared(Prot.S.lme_TP1)$Eta2)
Prot.S_TP1.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP1.res))
```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP1_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Prot.S.lme_TP1_KL)
eta_squared(Prot.S.lme_TP1_KL)


##SS
Prot.S.lme_TP1_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Prot.S.lme_TP1_SS)
eta_squared(Prot.S.lme_TP1_SS)


##Save results
Prot.S_TP1.site.res<-data.frame(rbind(summary(Prot.S.lme_TP1_KL)$coefficients[-1,],
                                      summary(Prot.S.lme_TP1_SS)$coefficients[-1,]))
Prot.S_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP1.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP1_KL)$Eta2, eta_squared(Prot.S.lme_TP1_SS)$Eta2)
Prot.S_TP1.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP1.site.res))

##Combine results 
Prot.S_TP1.res<-rbind(Prot.S_TP1.res, Prot.S_TP1.site.res)

```



```{r Plot Protein Symbiont TP1}
##Summary statistics by Site and Origin
TP1_ProtSym.sum<-summarySE(Coral.TP1, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP1_ProtSym.plot<-ggplot(TP1_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(250, 550)+
  annotate("text", x=2, y=535, label="**", size=sig.sz, fontface="bold"); TP1_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_C)
shapiro.test(Coral.TP1$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP1<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Bio.C.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.C.lme_TP1, plot = F)
plot(Bio.C.lme_res_TP1)

##Model results
summary(Bio.C.lme_TP1)

eta_squared(Bio.C.lme_TP1)

##Save model results
Bio.C_TP1.res<-data.frame(summary(Bio.C.lme_TP1)$coefficients[-1,])
Bio.C_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP1.res$EtaSq<-c(eta_squared(Bio.C.lme_TP1)$Eta2)
Bio.C_TP1.res$Response<-rep("Biomass Host", nrow(Bio.C_TP1.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP1_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.C.lme_TP1_KL)
eta_squared(Bio.C.lme_TP1_KL)

##SS
Bio.C.lme_TP1_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.C.lme_TP1_SS)
eta_squared(Bio.C.lme_TP1_SS)

##Save results
Bio.C_TP1.site.res<-data.frame(rbind(summary(Bio.C.lme_TP1_KL)$coefficients[-1,],
                                      summary(Bio.C.lme_TP1_SS)$coefficients[-1,]))
Bio.C_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP1.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP1_KL)$Eta2, eta_squared(Bio.C.lme_TP1_SS)$Eta2)
Bio.C_TP1.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP1.site.res))

##Combine results 
Bio.C_TP1.res<-rbind(Bio.C_TP1.res, Bio.C_TP1.site.res)

```


```{r Plot Biomass Host TP1}
##Summary statistics by Site and Origin
TP1_BioHost.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP1_BioHost.plot<-ggplot(TP1_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz),
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position = "top")+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.8, 1.6)+
   annotate("text", x=1, y=1.3, label="*", size=sig.sz, fontface="bold"); TP1_BioHost.plot
```



#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP1$AFDW_mg.cm2_S)
shapiro.test(Coral.TP1$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP1<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Bio.S.lme_res_TP1 <- simulateResiduals(fittedModel = Bio.S.lme_TP1, plot = F)
plot(Bio.S.lme_res_TP1)

##Model results
summary(Bio.S.lme_TP1)

eta_squared(Bio.S.lme_TP1)

##Save model results
Bio.S_TP1.res<-data.frame(summary(Bio.S.lme_TP1)$coefficients[-1,])
Bio.S_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP1.res$EtaSq<-c(eta_squared(Bio.S.lme_TP1)$Eta2)
Bio.S_TP1.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP1.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP1_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Bio.S.lme_TP1_KL)
eta_squared(Bio.S.lme_TP1_KL)


##SS
Bio.S.lme_TP1_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Bio.S.lme_TP1_SS)
eta_squared(Bio.S.lme_TP1_SS)


##Save results
Bio.S_TP1.site.res<-data.frame(rbind(summary(Bio.S.lme_TP1_KL)$coefficients[-1,],
                                     summary(Bio.S.lme_TP1_SS)$coefficients[-1,]))
Bio.S_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP1.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP1_KL)$Eta2, eta_squared(Bio.S.lme_TP1_SS)$Eta2)
Bio.S_TP1.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP1.site.res))

##Combine results 
Bio.S_TP1.res<-rbind(Bio.S_TP1.res, Bio.S_TP1.site.res)

```


```{r Plot Biomass Symbiont TP1}
##Summary statistics by Site and Origin
TP1_BioSym.sum<-summarySE(Coral.TP1, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP1_BioSym.plot<-ggplot(TP1_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.45, 0.95)+
  annotate("text", x=2, y=0.8, label="**", size=sig.sz, fontface="bold"); TP1_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP1$Chl_ug.cm2)
shapiro.test(Coral.TP1$Chl_ug.cm2)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP1<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP1)

##Check residuals
Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Chl.lme_TP1, plot = F)
plot(Chl.lme_res_TP1)

##Model results
summary(Chl.lme_TP1)

eta_squared(Chl.lme_TP1)

##Save model results
Chl_TP1.res<-data.frame(summary(Chl.lme_TP1)$coefficients[-1,])
Chl_TP1.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP1.res$EtaSq<-c(eta_squared(Chl.lme_TP1)$Eta2)
Chl_TP1.res$Response<-rep("Chlorophyll", nrow(Chl_TP1.res))

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP1_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="KL"),])
summary(Chl.lme_TP1_KL)
eta_squared(Chl.lme_TP1_KL)

##SS
Chl.lme_TP1_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP1[which(Coral.TP1$Site=="SS"),])
summary(Chl.lme_TP1_SS)
eta_squared(Chl.lme_TP1_SS)

##Save results
Chl_TP1.site.res<-data.frame(rbind(summary(Chl.lme_TP1_KL)$coefficients[-1,],
                                     summary(Chl.lme_TP1_SS)$coefficients[-1,]))
Chl_TP1.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP1.site.res$EtaSq<-c(eta_squared(Chl.lme_TP1_KL)$Eta2, eta_squared(Chl.lme_TP1_SS)$Eta2)
Chl_TP1.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP1.site.res))

##Combine results 
Chl_TP1.res<-rbind(Chl_TP1.res, Chl_TP1.site.res)

```


```{r Plot Chlorophyll TP1}
##Summary statistics by Site and Origin
TP1_Chl.sum<-summarySE(Coral.TP1, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP1_Chl.plot<-ggplot(TP1_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(0.5, 3.25)+
  annotate("text", x=2, y=1.7, label="***", size=sig.sz, fontface="bold"); TP1_Chl.plot
```


#### Save Results
```{r}
##Combine Results
Phys_T1.res<-rbind(Prot.C_TP1.res, Prot.S_TP1.res, Bio.C_TP1.res, Bio.S_TP1.res, Chl_TP1.res)

##Add Timepoint
Phys_T1.res$TimeP<-rep("TP1", nrow(Phys_T1.res))

```


### TP2

```{r}
##Subset Timepoint 2 
Coral.TP2<-subset(Coral.rm, TimeP=="TP2")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP2$TP_ug.cm2_C)
shapiro.test(Coral.TP2$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP2<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Prot.C.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.C.lme_TP2, plot = F)
plot(Prot.C.lme_res_TP2)

##Model results
summary(Prot.C.lme_TP2)

eta_squared(Prot.C.lme_TP2)

##Save model results
Prot.C_TP2.res<-data.frame(summary(Prot.C.lme_TP2)$coefficients[-1,])
Prot.C_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP2.res$EtaSq<-c(eta_squared(Prot.C.lme_TP2)$Eta2)
Prot.C_TP2.res$Response<-rep("Protein Host", nrow(Prot.C_TP2.res))

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP2$TP_ug.cm2_S)
shapiro.test(Coral.TP2$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP2<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Prot.S.lme_res_TP2 <- simulateResiduals(fittedModel = Prot.S.lme_TP2, plot = F)
plot(Prot.S.lme_res_TP2)

##Model results
summary(Prot.S.lme_TP2)

eta_squared(Prot.S.lme_TP2)

##Save model results
Prot.S_TP2.res<-data.frame(summary(Prot.S.lme_TP2)$coefficients[-1,])
Prot.S_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP2.res$EtaSq<-c(eta_squared(Prot.S.lme_TP2)$Eta2)
Prot.S_TP2.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP2.res))

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP2_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Prot.S.lme_TP2_KL)
eta_squared(Prot.S.lme_TP2_KL)

##SS
Prot.S.lme_TP2_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Prot.S.lme_TP2_SS)
eta_squared(Prot.S.lme_TP2_SS)

##Save results
Prot.S_TP2.site.res<-data.frame(rbind(summary(Prot.S.lme_TP2_KL)$coefficients[-1,],
                                     summary(Prot.S.lme_TP2_SS)$coefficients[-1,]))
Prot.S_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP2.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP2_KL)$Eta2, eta_squared(Prot.S.lme_TP2_SS)$Eta2)
Prot.S_TP2.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP2.site.res))

##Combine results 
Prot.S_TP2.res<-rbind(Prot.S_TP2.res, Prot.S_TP2.site.res)

```



```{r Plot Protein Symbiont TP2}
##Summary statistics by Site and Origin
TP2_ProtSym.sum<-summarySE(Coral.TP2, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP2_ProtSym.plot<-ggplot(TP2_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(250, 550); TP2_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_C)
shapiro.test(Coral.TP2$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP2<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Bio.C.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.C.lme_TP2, plot = F)
plot(Bio.C.lme_res_TP2)

##Model results
summary(Bio.C.lme_TP2)

eta_squared(Bio.C.lme_TP2)

##Save model results
Bio.C_TP2.res<-data.frame(summary(Bio.C.lme_TP2)$coefficients[-1,])
Bio.C_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP2.res$EtaSq<-c(eta_squared(Bio.C.lme_TP2)$Eta2)
Bio.C_TP2.res$Response<-rep("Biomass Host", nrow(Bio.C_TP2.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP2_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.C.lme_TP2_KL)
eta_squared(Bio.C.lme_TP2_KL)

##SS
Bio.C.lme_TP2_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.C.lme_TP2_SS)
eta_squared(Bio.C.lme_TP2_SS)

##Save results
Bio.C_TP2.site.res<-data.frame(rbind(summary(Bio.C.lme_TP2_KL)$coefficients[-1,],
                                     summary(Bio.C.lme_TP2_SS)$coefficients[-1,]))
Bio.C_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP2.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP2_KL)$Eta2, eta_squared(Bio.C.lme_TP2_SS)$Eta2)
Bio.C_TP2.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP2.site.res))

##Combine results 
Bio.C_TP2.res<-rbind(Bio.C_TP2.res, Bio.C_TP2.site.res)

```


```{r Plot Biomass Host TP2}
##Summary statistics by Site and Origin
TP2_BioHost.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP2_BioHost.plot<-ggplot(TP2_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.8, 1.6)+
  annotate("text", x=2, y=1.35, label="-", size=levels.sz, fontface="bold"); TP2_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP2$AFDW_mg.cm2_S)
shapiro.test(Coral.TP2$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP2<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Bio.S.lme_res_TP2 <- simulateResiduals(fittedModel = Bio.S.lme_TP2, plot = F)
plot(Bio.S.lme_res_TP2)

##Model results
summary(Bio.S.lme_TP2)

eta_squared(Bio.S.lme_TP2)

##Save model results
Bio.S_TP2.res<-data.frame(summary(Bio.S.lme_TP2)$coefficients[-1,])
Bio.S_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP2.res$EtaSq<-c(eta_squared(Bio.S.lme_TP2)$Eta2)
Bio.S_TP2.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP2.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP2_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Bio.S.lme_TP2_KL)
eta_squared(Bio.S.lme_TP2_KL)

##SS
Bio.S.lme_TP2_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Bio.S.lme_TP2_SS)
eta_squared(Bio.S.lme_TP2_SS)

##Save results
Bio.S_TP2.site.res<-data.frame(rbind(summary(Bio.S.lme_TP2_KL)$coefficients[-1,],
                                     summary(Bio.S.lme_TP2_SS)$coefficients[-1,]))
Bio.S_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP2.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP2_KL)$Eta2, eta_squared(Bio.S.lme_TP2_SS)$Eta2)
Bio.S_TP2.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP2.site.res))

##Combine results 
Bio.S_TP2.res<-rbind(Bio.S_TP2.res, Bio.S_TP2.site.res)

```


```{r Plot Biomass Symbiont TP2}
##Summary statistics by Site and Origin
TP2_BioSym.sum<-summarySE(Coral.TP2, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP2_BioSym.plot<-ggplot(TP2_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position= "top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.45, 0.95)+
  annotate("text", x=1, y=0.78, label="*", size=sig.sz, fontface="bold"); TP2_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP2$Chl_ug.cm2)
shapiro.test(Coral.TP2$Chl_ug.cm2)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP2<-lmer(Chl_ug.cm2~Origin*Site+(1|Genotype), data=Coral.TP2)

##Check residuals
Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Chl.lme_TP2, plot = F)
plot(Chl.lme_res_TP2)

##Model results
summary(Chl.lme_TP2)

eta_squared(Chl.lme_TP2)

##Save model results
Chl_TP2.res<-data.frame(summary(Chl.lme_TP2)$coefficients[-1,])
Chl_TP2.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP2.res$EtaSq<-c(eta_squared(Chl.lme_TP2)$Eta2)
Chl_TP2.res$Response<-rep("Chlorophyll", nrow(Chl_TP2.res))

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP2_KL<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="KL"),])
summary(Chl.lme_TP2_KL)
eta_squared(Chl.lme_TP2_KL)


##SS
Chl.lme_TP2_SS<-lmer(Chl_ug.cm2~Origin+(1|Genotype), data=Coral.TP2[which(Coral.TP2$Site=="SS"),])
summary(Chl.lme_TP2_SS)
eta_squared(Chl.lme_TP2_SS)


##Save results
Chl_TP2.site.res<-data.frame(rbind(summary(Chl.lme_TP2_KL)$coefficients[-1,],
                                     summary(Chl.lme_TP2_SS)$coefficients[-1,]))
Chl_TP2.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP2.site.res$EtaSq<-c(eta_squared(Chl.lme_TP2_KL)$Eta2, eta_squared(Chl.lme_TP2_SS)$Eta2)
Chl_TP2.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP2.site.res))

##Combine results 
Chl_TP2.res<-rbind(Chl_TP2.res, Chl_TP2.site.res)

```


```{r Plot Chlorophyll TP2}
##Summary statistics by Site and Origin
TP2_Chl.sum<-summarySE(Coral.TP2, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP2_Chl.plot<-ggplot(TP2_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position= "top")+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(0.5, 3.25)+
  annotate("text", x=c(1,2), y=c(1.5, 2.6), label="*", size=sig.sz, fontface="bold"); TP2_Chl.plot
```


#### Save Results
```{r}
##Combine Results
Phys_T2.res<-rbind(Prot.C_TP2.res, Prot.S_TP2.res, Bio.C_TP2.res, Bio.S_TP2.res, Chl_TP2.res)

##Add Timepoint
Phys_T2.res$TimeP<-rep("TP2", nrow(Phys_T2.res))

```


### TP3

```{r}
##Subset Timepoint 3 
Coral.TP3<-subset(Coral.rm, TimeP=="TP3")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP3$TP_ug.cm2_C)
shapiro.test(Coral.TP3$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP3<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Prot.C.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.C.lme_TP3, plot = F)
plot(Prot.C.lme_res_TP3)

##Model results
summary(Prot.C.lme_TP3)

eta_squared(Prot.C.lme_TP3)

##Save model results
Prot.C_TP3.res<-data.frame(summary(Prot.C.lme_TP3)$coefficients[-1,])
Prot.C_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP3.res$EtaSq<-c(eta_squared(Prot.C.lme_TP3)$Eta2)
Prot.C_TP3.res$Response<-rep("Protein Host", nrow(Prot.C_TP3.res))

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP3$TP_ug.cm2_S)
shapiro.test(Coral.TP3$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP3<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Prot.S.lme_res_TP3 <- simulateResiduals(fittedModel = Prot.S.lme_TP3, plot = F)
plot(Prot.S.lme_res_TP3)

##Model results
summary(Prot.S.lme_TP3)

eta_squared(Prot.S.lme_TP3)

##Save model results
Prot.S_TP3.res<-data.frame(summary(Prot.S.lme_TP3)$coefficients[-1,])
Prot.S_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP3.res$EtaSq<-c(eta_squared(Prot.S.lme_TP3)$Eta2)
Prot.S_TP3.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP3.res))

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP3_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Prot.S.lme_TP3_KL)
eta_squared(Prot.S.lme_TP3_KL)


##SS
Prot.S.lme_TP3_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Prot.S.lme_TP3_SS)
eta_squared(Prot.S.lme_TP3_SS)

##Save results
Prot.S_TP3.site.res<-data.frame(rbind(summary(Prot.S.lme_TP3_KL)$coefficients[-1,],
                                     summary(Prot.S.lme_TP3_SS)$coefficients[-1,]))
Prot.S_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP3.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP3_KL)$Eta2, eta_squared(Prot.S.lme_TP3_SS)$Eta2)
Prot.S_TP3.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP3.site.res))

##Combine results 
Prot.S_TP3.res<-rbind(Prot.S_TP3.res, Prot.S_TP3.site.res)

```



```{r Plot Protein Symbiont TP3}
##Summary statistics by Site and Origin
TP3_ProtSym.sum<-summarySE(Coral.TP3, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP3_ProtSym.plot<-ggplot(TP3_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(250, 550); TP3_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_C)
shapiro.test(Coral.TP3$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP3<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Bio.C.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.C.lme_TP3, plot = F)
plot(Bio.C.lme_res_TP3)

##Model results
summary(Bio.C.lme_TP3)

eta_squared(Bio.C.lme_TP3)

##Save model results
Bio.C_TP3.res<-data.frame(summary(Bio.C.lme_TP3)$coefficients[-1,])
Bio.C_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP3.res$EtaSq<-c(eta_squared(Bio.C.lme_TP3)$Eta2)
Bio.C_TP3.res$Response<-rep("Biomass Host", nrow(Bio.C_TP3.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.C.lme_TP3_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.C.lme_TP3_KL)
eta_squared(Bio.C.lme_TP3_KL)

##SS
Bio.C.lme_TP3_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.C.lme_TP3_SS)
eta_squared(Bio.C.lme_TP3_SS)

##Save results
Bio.C_TP3.site.res<-data.frame(rbind(summary(Bio.C.lme_TP3_KL)$coefficients[-1,],
                                     summary(Bio.C.lme_TP3_SS)$coefficients[-1,]))
Bio.C_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP3.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP3_KL)$Eta2, eta_squared(Bio.C.lme_TP3_SS)$Eta2)
Bio.C_TP3.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP3.site.res))

##Combine results 
Bio.C_TP3.res<-rbind(Bio.C_TP3.res, Bio.C_TP3.site.res)

```


```{r Plot Biomass Host TP3}
##Summary statistics by Site and Origin
TP3_BioHost.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP3_BioHost.plot<-ggplot(TP3_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.8, 1.6); TP3_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP3$AFDW_mg.cm2_S)
shapiro.test(Coral.TP3$AFDW_mg.cm2_S)
#Slightly non Normal

hist(log(Coral.TP3$AFDW_mg.cm2_S+1))
shapiro.test(log(Coral.TP3$AFDW_mg.cm2_S+1))
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP3<-lmer(log(AFDW_mg.cm2_S+1)~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Bio.S.lme_res_TP3 <- simulateResiduals(fittedModel = Bio.S.lme_TP3, plot = F)
plot(Bio.S.lme_res_TP3)

##Model results
summary(Bio.S.lme_TP3)

eta_squared(Bio.S.lme_TP3)

##Save model results
Bio.S_TP3.res<-data.frame(summary(Bio.S.lme_TP3)$coefficients[-1,])
Bio.S_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP3.res$EtaSq<-c(eta_squared(Bio.S.lme_TP3)$Eta2)
Bio.S_TP3.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP3.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP3_KL<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Bio.S.lme_TP3_KL)
eta_squared(Bio.S.lme_TP3_KL)

##SS
Bio.S.lme_TP3_SS<-lmer(log(AFDW_mg.cm2_S+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Bio.S.lme_TP3_SS)
eta_squared(Bio.S.lme_TP3_SS)

##Save results
Bio.S_TP3.site.res<-data.frame(rbind(summary(Bio.S.lme_TP3_KL)$coefficients[-1,],
                                     summary(Bio.S.lme_TP3_SS)$coefficients[-1,]))
Bio.S_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP3.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP3_KL)$Eta2, eta_squared(Bio.S.lme_TP3_SS)$Eta2)
Bio.S_TP3.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP3.site.res))

##Combine results 
Bio.S_TP3.res<-rbind(Bio.S_TP3.res, Bio.S_TP3.site.res)

```


```{r Plot Biomass Symbiont TP3}
##Summary statistics by Site and Origin
TP3_BioSym.sum<-summarySE(Coral.TP3, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP3_BioSym.plot<-ggplot(TP3_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.45, 0.95); TP3_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP3$Chl_ug.cm2)
shapiro.test(Coral.TP3$Chl_ug.cm2)
#Not normal

hist(log(Coral.TP3$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP3$Chl_ug.cm2+1))
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP3<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP3)

##Check residuals
Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Chl.lme_TP3, plot = F)
plot(Chl.lme_res_TP3)

##Model results
summary(Chl.lme_TP3)

eta_squared(Chl.lme_TP3)

##Save model results
Chl_TP3.res<-data.frame(summary(Chl.lme_TP3)$coefficients[-1,])
Chl_TP3.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP3.res$EtaSq<-c(eta_squared(Chl.lme_TP3)$Eta2)
Chl_TP3.res$Response<-rep("Chlorophyll", nrow(Chl_TP3.res))

```


Effect size of Origin for each Site
```{r}
##KL
Chl.lme_TP3_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="KL"),])
summary(Chl.lme_TP3_KL)
eta_squared(Chl.lme_TP3_KL)


##SS
Chl.lme_TP3_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP3[which(Coral.TP3$Site=="SS"),])
summary(Chl.lme_TP3_SS)
eta_squared(Chl.lme_TP3_SS)

##Save results
Chl_TP3.site.res<-data.frame(rbind(summary(Chl.lme_TP3_KL)$coefficients[-1,],
                                     summary(Chl.lme_TP3_SS)$coefficients[-1,]))
Chl_TP3.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP3.site.res$EtaSq<-c(eta_squared(Chl.lme_TP3_KL)$Eta2, eta_squared(Chl.lme_TP3_SS)$Eta2)
Chl_TP3.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP3.site.res))

##Combine results 
Chl_TP3.res<-rbind(Chl_TP3.res, Chl_TP3.site.res)

```


```{r Plot Chlorophyll TP3}
##Summary statistics by Site and Origin
TP3_Chl.sum<-summarySE(Coral.TP3, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP3_Chl.plot<-ggplot(TP3_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(0.5, 3.25)+
  annotate("text", x=1, y=1.5, label="-", size=levels.sz, fontface="bold"); TP3_Chl.plot
```


#### Save Results
```{r}
##Combine Results
Phys_T3.res<-rbind(Prot.C_TP3.res, Prot.S_TP3.res, Bio.C_TP3.res, Bio.S_TP3.res, Chl_TP3.res)

##Add Timepoint
Phys_T3.res$TimeP<-rep("TP3", nrow(Phys_T3.res))

```


### TP4

```{r}
##Subset Timepoint 4 
Coral.TP4<-subset(Coral.rm, TimeP=="TP4")

```


#### Protein Host 
```{r}
##Check normality
hist(Coral.TP4$TP_ug.cm2_C)
shapiro.test(Coral.TP4$TP_ug.cm2_C)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.C.lme_TP4<-lmer(TP_ug.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Prot.C.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.C.lme_TP4, plot = F)
plot(Prot.C.lme_res_TP4)

##Model results
summary(Prot.C.lme_TP4)

eta_squared(Prot.C.lme_TP4)

##Save model results
Prot.C_TP4.res<-data.frame(summary(Prot.C.lme_TP4)$coefficients[-1,])
Prot.C_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.C_TP4.res$EtaSq<-c(eta_squared(Prot.C.lme_TP4)$Eta2)
Prot.C_TP4.res$Response<-rep("Protein Host", nrow(Prot.C_TP4.res))

```


#### Protein Symbiont 
```{r}
##Check normality
hist(Coral.TP4$TP_ug.cm2_S)
shapiro.test(Coral.TP4$TP_ug.cm2_S)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Prot.S.lme_TP4<-lmer(TP_ug.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Prot.S.lme_res_TP4 <- simulateResiduals(fittedModel = Prot.S.lme_TP4, plot = F)
plot(Prot.S.lme_res_TP4)

##Model results
summary(Prot.S.lme_TP4)

eta_squared(Prot.S.lme_TP4)

##Save model results
Prot.S_TP4.res<-data.frame(summary(Prot.S.lme_TP4)$coefficients[-1,])
Prot.S_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Prot.S_TP4.res$EtaSq<-c(eta_squared(Prot.S.lme_TP4)$Eta2)
Prot.S_TP4.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP4.res))

```

Effect size of Origin for each Site
```{r}
##KL
Prot.S.lme_TP4_KL<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Prot.S.lme_TP4_KL)
eta_squared(Prot.S.lme_TP4_KL)

##SS
Prot.S.lme_TP4_SS<-lmer(TP_ug.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Prot.S.lme_TP4_SS)
eta_squared(Prot.S.lme_TP4_SS)

##Save results
Prot.S_TP4.site.res<-data.frame(rbind(summary(Prot.S.lme_TP4_KL)$coefficients[-1,],
                                     summary(Prot.S.lme_TP4_SS)$coefficients[-1,]))
Prot.S_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Prot.S_TP4.site.res$EtaSq<-c(eta_squared(Prot.S.lme_TP4_KL)$Eta2, eta_squared(Prot.S.lme_TP4_SS)$Eta2)
Prot.S_TP4.site.res$Response<-rep("Protein Symbiont", nrow(Prot.S_TP4.site.res))

##Combine results 
Prot.S_TP4.res<-rbind(Prot.S_TP4.res, Prot.S_TP4.site.res)

```



```{r Plot Protein Symbiont TP4}
##Summary statistics by Site and Origin
TP4_ProtSym.sum<-summarySE(Coral.TP4, measurevar="TP_ug.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Protein across Treatments
TP4_ProtSym.plot<-ggplot(TP4_ProtSym.sum, aes(x=Site, y=TP_ug.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TP_ug.cm2_S-se, ymax=TP_ug.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Protein (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(250, 550); TP4_ProtSym.plot
```


#### Biomass Host 
```{r}
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_C)
shapiro.test(Coral.TP4$AFDW_mg.cm2_C)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.C.lme_TP4<-lmer(AFDW_mg.cm2_C~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Bio.C.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.C.lme_TP4, plot = F)
plot(Bio.C.lme_res_TP4)

##Model results
summary(Bio.C.lme_TP4)

eta_squared(Bio.C.lme_TP4)

##Save model results
Bio.C_TP4.res<-data.frame(summary(Bio.C.lme_TP4)$coefficients[-1,])
Bio.C_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.C_TP4.res$EtaSq<-c(eta_squared(Bio.C.lme_TP4)$Eta2)
Bio.C_TP4.res$Response<-rep("Biomass Host", nrow(Bio.C_TP4.res))

```


Effect size of Origin for each Site
```{r}

##KL
Bio.C.lme_TP4_KL<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Bio.C.lme_TP4_KL)
eta_squared(Bio.C.lme_TP4_KL)

##SS
Bio.C.lme_TP4_SS<-lmer(AFDW_mg.cm2_C~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.C.lme_TP4_SS)
eta_squared(Bio.C.lme_TP4_SS)

##Save results
Bio.C_TP4.site.res<-data.frame(rbind(summary(Bio.C.lme_TP4_KL)$coefficients[-1,],
                                     summary(Bio.C.lme_TP4_SS)$coefficients[-1,]))
Bio.C_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.C_TP4.site.res$EtaSq<-c(eta_squared(Bio.C.lme_TP4_KL)$Eta2, eta_squared(Bio.C.lme_TP4_SS)$Eta2)
Bio.C_TP4.site.res$Response<-rep("Biomass Host", nrow(Bio.C_TP4.site.res))

##Combine results 
Bio.C_TP4.res<-rbind(Bio.C_TP4.res, Bio.C_TP4.site.res)

```


```{r Plot Biomass Host TP4}
##Summary statistics by Site and Origin
TP4_BioHost.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_C", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Host Biomass across Treatments
TP4_BioHost.plot<-ggplot(TP4_BioHost.sum, aes(x=Site, y=AFDW_mg.cm2_C, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_C-se, ymax=AFDW_mg.cm2_C+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Host Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.8, 1.6)+
  annotate("text", x=2, y=1.45, label="*", size=sig.sz, fontface="bold"); TP4_BioHost.plot
```


#### Biomass Symbionts 
```{r}
##Check normality
hist(Coral.TP4$AFDW_mg.cm2_S)
shapiro.test(Coral.TP4$AFDW_mg.cm2_S)
#Normal

##Model 
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Bio.S.lme_TP4<-lmer(AFDW_mg.cm2_S~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Bio.S.lme_res_TP4 <- simulateResiduals(fittedModel = Bio.S.lme_TP4, plot = F)
plot(Bio.S.lme_res_TP4)

##Model results
summary(Bio.S.lme_TP4)

eta_squared(Bio.S.lme_TP4)

##Save model results
Bio.S_TP4.res<-data.frame(summary(Bio.S.lme_TP4)$coefficients[-1,])
Bio.S_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Bio.S_TP4.res$EtaSq<-c(eta_squared(Bio.S.lme_TP4)$Eta2)
Bio.S_TP4.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP4.res))

```


Effect size of Origin for each Site
```{r}
##KL
Bio.S.lme_TP4_KL<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Bio.S.lme_TP4_KL)
eta_squared(Bio.S.lme_TP4_KL)

##SS
Bio.S.lme_TP4_SS<-lmer(AFDW_mg.cm2_S~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Bio.S.lme_TP4_SS)
eta_squared(Bio.S.lme_TP4_SS)

##Save results
Bio.S_TP4.site.res<-data.frame(rbind(summary(Bio.S.lme_TP4_KL)$coefficients[-1,],
                                     summary(Bio.S.lme_TP4_SS)$coefficients[-1,]))
Bio.S_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Bio.S_TP4.site.res$EtaSq<-c(eta_squared(Bio.S.lme_TP4_KL)$Eta2, eta_squared(Bio.S.lme_TP4_SS)$Eta2)
Bio.S_TP4.site.res$Response<-rep("Biomass Symbiont", nrow(Bio.S_TP4.site.res))

##Combine results 
Bio.S_TP4.res<-rbind(Bio.S_TP4.res, Bio.S_TP4.site.res)

```


```{r Plot Biomass Symbiont TP4}
##Summary statistics by Site and Origin
TP4_BioSym.sum<-summarySE(Coral.TP4, measurevar="AFDW_mg.cm2_S", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Biomass across Treatments
TP4_BioSym.plot<-ggplot(TP4_BioSym.sum, aes(x=Site, y=AFDW_mg.cm2_S, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=AFDW_mg.cm2_S-se, ymax=AFDW_mg.cm2_S+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Symbiont Biomass (mg cm'^-2*")")), colour=NULL)+
  ylim(0.45, 0.95); TP4_BioSym.plot
```


#### Chlorophyll
```{r}
##Check normality
hist(Coral.TP4$Chl_ug.cm2)
shapiro.test(Coral.TP4$Chl_ug.cm2)
#Slightly non normal

hist(log(Coral.TP4$Chl_ug.cm2+1))
shapiro.test(log(Coral.TP4$Chl_ug.cm2+1))
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interactions between Origin and Site
Chl.lme_TP4<-lmer(log(Chl_ug.cm2+1)~Origin*Site+(1|Genotype), data=Coral.TP4)

##Check residuals
Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Chl.lme_TP4, plot = F)
plot(Chl.lme_res_TP4)

##Model results
summary(Chl.lme_TP4)

eta_squared(Chl.lme_TP4)

##Save model results
Chl_TP4.res<-data.frame(summary(Chl.lme_TP4)$coefficients[-1,])
Chl_TP4.res$Predictor<-c("Origin", "Site", "Origin x Site")
Chl_TP4.res$EtaSq<-c(eta_squared(Chl.lme_TP4)$Eta2)
Chl_TP4.res$Response<-rep("Chlorophyll", nrow(Chl_TP4.res))

```


Effect size of Origin for each Site
```{r}

##KL
Chl.lme_TP4_KL<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="KL"),])
summary(Chl.lme_TP4_KL)
eta_squared(Chl.lme_TP4_KL)


##SS
Chl.lme_TP4_SS<-lmer(log(Chl_ug.cm2+1)~Origin+(1|Genotype), data=Coral.TP4[which(Coral.TP4$Site=="SS"),])
summary(Chl.lme_TP4_SS)
eta_squared(Chl.lme_TP4_SS)

##Save results
Chl_TP4.site.res<-data.frame(rbind(summary(Chl.lme_TP4_KL)$coefficients[-1,],
                                     summary(Chl.lme_TP4_SS)$coefficients[-1,]))
Chl_TP4.site.res$Predictor<-c("KL Origin", "SS Origin")
Chl_TP4.site.res$EtaSq<-c(eta_squared(Chl.lme_TP4_KL)$Eta2, eta_squared(Chl.lme_TP4_SS)$Eta2)
Chl_TP4.site.res$Response<-rep("Chlorophyll", nrow(Chl_TP4.site.res))

##Combine results 
Chl_TP4.res<-rbind(Chl_TP4.res, Chl_TP4.site.res)

```


```{r Plot Chlorophyll TP4}
##Summary statistics by Site and Origin
TP4_Chl.sum<-summarySE(Coral.TP4, measurevar="Chl_ug.cm2", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
TP4_Chl.plot<-ggplot(TP4_Chl.sum, aes(x=Site, y=Chl_ug.cm2, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position="top")+
  labs(x="Site and Origin", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour=NULL)+
  ylim(0.5, 3.25); TP4_Chl.plot
```


#### Save Results
```{r}
##Combine Results
Phys_T4.res<-rbind(Prot.C_TP4.res, Prot.S_TP4.res, Bio.C_TP4.res, Bio.S_TP4.res, Chl_TP4.res)

##Add Timepoint
Phys_T4.res$TimeP<-rep("TP4", nrow(Phys_T4.res))

```


### Effect Size over Time
```{r}
##Dataframe of effect size results
Phys.ES<-rbind(Phys_T1.res, Phys_T2.res, Phys_T3.res, Phys_T4.res)
Phys.ES<-Phys.ES %>% dplyr::rename(p = Pr...t..)
Phys.ES<-Phys.ES[,c("TimeP", "Predictor", "Response", "EtaSq", "p")]

##Site specific results
Phys.ES<-Phys.ES[which(Phys.ES$Predictor=="KL Origin" | Phys.ES$Predictor=="SS Origin"),]
Phys.ES<-Phys.ES %>% separate_wider_delim(cols="Predictor", delim=" ", names=c("Site", "Predictor"), cols_remove = TRUE)

##Add Metric names
Phys.ES$Metric<-ifelse(Phys.ES$Response== "Protein Host", "TP_ug.cm2_C", ifelse(
  Phys.ES$Response== "Protein Symbiont", "TP_ug.cm2_S", ifelse(
    Phys.ES$Response=="Biomass Host", "AFDW_mg.cm2_C", ifelse(
      Phys.ES$Response=="Biomass Symbiont", "AFDW_mg.cm2_S", ifelse(
        Phys.ES$Response=="Chlorophyll", "Chl_ug.cm2", NA)))))

##Add Significance levels
Phys.ES$Sig<-ifelse(Phys.ES$p<0.001, "***", ifelse(Phys.ES$p<0.01, "**", 
            ifelse(Phys.ES$p<0.05, "*", ifelse(Phys.ES$p<0.1, "-", NA))))

```


#### Host Biomass
```{r Plot Effect Size Host Biomass}
Bio.C.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_C"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Host Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), 
        axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"),
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position=c(.9, .8))+
   labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.C.ES.plot

```

#### Symbiont Biomass
```{r Plot Effect Size Symbiont Biomass}
Bio.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="AFDW_mg.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Biomass")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), 
        axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"),
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position=c(.9, .8))+
   labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Bio.S.ES.plot

```


#### Symbiont Protein
```{r Plot Effect Size Symbiont Protein}
Prot.S.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="TP_ug.cm2_S"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Protein")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), 
        axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"),
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position=c(.9, .8))+
   labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Prot.S.ES.plot

```


#### Chlorophyll
```{r Plot Effect Size Chlorophyll}
Chl.ES.plot<-ggplot(Phys.ES[which(Phys.ES$Metric=="Chl_ug.cm2"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Chlorophyll")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), 
        axis.title.x = element_text(size = axis.title.sz), 
        axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"),
        axis.text.y=element_text(size=axis.txt.sz, colour="black"),
        legend.text=element_text(size=leg.txt.sz), 
        legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), 
        legend.position=c(.9, .8))+
   labs(x="Time Point", y=expression(paste("Origin Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.6)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Chl.ES.plot

```


# Figures

### Figure 3 Univariate Physiology

#### Adjust for Panel
```{r}
##Biomass Host
TP1_BioHost.plot<-TP1_BioHost.plot+ ggtitle("TP1")+ labs(x="")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5), 
        legend.position=c(0.5, 0.85), legend.direction="horizontal")+
  guides(color=guide_legend(nrow=2, byrow=FALSE)) 

TP2_BioHost.plot<-TP2_BioHost.plot+ ggtitle("TP2")+ labs(x="", y="") +
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5), 
        legend.position="none")

TP3_BioHost.plot<-TP3_BioHost.plot+ ggtitle("TP3")+ labs(x="", y="") +
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5), 
        legend.position="none")

TP4_BioHost.plot<-TP4_BioHost.plot+ ggtitle("TP4")+ labs(x="", y="") +
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5), 
        legend.position="none")

Bio.C.ES.plot<-Bio.C.ES.plot + theme(legend.position=c(.15, .8))

##Biomass Symbiont
TP1_BioSym.plot<-TP1_BioSym.plot + labs(x="") + theme(legend.position="none")

TP2_BioSym.plot<-TP2_BioSym.plot + labs(x="", y="") + theme(legend.position="none")

TP3_BioSym.plot<-TP3_BioSym.plot + labs(x="", y="") + theme(legend.position="none")

TP4_BioSym.plot<-TP4_BioSym.plot + labs(x="", y="") + theme(legend.position="none")

Bio.S.ES.plot<-Bio.S.ES.plot + theme(legend.position="none") 

##Protein Symbiont
TP1_ProtSym.plot<-TP1_ProtSym.plot + labs(x="") + theme(legend.position="none")

TP2_ProtSym.plot<-TP2_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")

TP3_ProtSym.plot<-TP3_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")

TP4_ProtSym.plot<-TP4_ProtSym.plot + labs(x="", y="") + theme(legend.position="none")

Prot.S.ES.plot<-Prot.S.ES.plot + theme(legend.position="none") 

##Chlorophyll
TP1_Chl.plot<-TP1_Chl.plot + theme(legend.position="none")

TP2_Chl.plot<-TP2_Chl.plot + labs(y="") + theme(legend.position="none")

TP3_Chl.plot<-TP3_Chl.plot + labs(y="") + theme(legend.position="none")

TP4_Chl.plot<-TP4_Chl.plot + labs(y="") + theme(legend.position="none")

Chl.ES.plot<-Chl.ES.plot + theme(legend.position="none")
```


#### Figure 3
```{r}
##Create Panel
Phys_fig<-plot_grid(TP1_BioHost.plot, TP2_BioHost.plot, 
                      TP3_BioHost.plot, TP4_BioHost.plot, Bio.C.ES.plot, 
                      TP1_BioSym.plot, TP2_BioSym.plot, 
                      TP3_BioSym.plot, TP4_BioSym.plot, Bio.S.ES.plot, 
                      TP1_ProtSym.plot, TP2_ProtSym.plot, 
                      TP3_ProtSym.plot, TP4_ProtSym.plot, Prot.S.ES.plot, 
                      TP1_Chl.plot, TP2_Chl.plot, 
                      TP3_Chl.plot, TP4_Chl.plot, Chl.ES.plot,
                    rel_widths=c(0.8, 0.8, 0.8, 0.8, 1), rel_heights = 1,
                    nrow=4, ncol=5, byrow=T, labels = NULL)

##Save Figure
ggsave(filename="Figures/02_Physiology/Fig3_Physiology.png", plot=Phys_fig, dpi=300, width=16, height=16, units="in")

```


# Tables 

### Table S3A Physiology PERMANOVA and PERMDISP
```{r}
##Combine Results Tables
TableS3A_Phys.PERM<-data.frame(rbind(PERM_T1.res, PERM_T2.res, PERM_T3.res, PERM_T4.res))

##Organize
names(TableS3A_Phys.PERM)
TableS3A_Phys.PERM<-TableS3A_Phys.PERM %>% dplyr::rename( DF = Df, SS = SumOfSqs, EffectSize = parOmegaSq, p = Pr..F.)
TableS3A_Phys.PERM<-TableS3A_Phys.PERM[,c("Timepoint", "Predictor", "DF", "SS", "F",   "EffectSize", "p", "p_DISP")]

#Round to 3 digits
TableS3A_Phys.PERM$SS<-round(TableS3A_Phys.PERM$SS, 3)
TableS3A_Phys.PERM$F<-round(TableS3A_Phys.PERM$F, 3)
TableS3A_Phys.PERM$EffectSize<-round(TableS3A_Phys.PERM$EffectSize, 3)
TableS3A_Phys.PERM$p<-round(TableS3A_Phys.PERM$p, 3)
TableS3A_Phys.PERM$p_DISP<-round(TableS3A_Phys.PERM$p_DISP, 3)

##Write Out Table
write.csv(TableS3A_Phys.PERM, "Tables/TableS3A_Physiology_PERM_Results.csv", row.names=FALSE)


```


### Table S4 Physiology Univariate LM
```{r}
##Combine Results Tables
TableS4_Phys.LM<-data.frame(rbind(Phys_T1.res, Phys_T2.res, Phys_T3.res, Phys_T4.res))

##Organize
names(TableS4_Phys.LM)
TableS4_Phys.LM<-TableS4_Phys.LM %>% dplyr::rename(SE = Std..Error, DF = df, t = t.value, p = Pr...t.., EffectSize = EtaSq, Timepoint = TimeP)
TableS4_Phys.LM<-TableS4_Phys.LM[,c("Timepoint", "Response", "Predictor", "Estimate", "SE", "DF", "t", "p", "EffectSize")]

#Round to 3 digits
TableS4_Phys.LM$Estimate<-round(TableS4_Phys.LM$Estimate, 3)
TableS4_Phys.LM$SE<-round(TableS4_Phys.LM$SE, 3)
TableS4_Phys.LM$t<-round(TableS4_Phys.LM$t, 3)
TableS4_Phys.LM$p<-round(TableS4_Phys.LM$p, 3)
TableS4_Phys.LM$EffectSize<-round(TableS4_Phys.LM$EffectSize, 3)

#Integer
TableS4_Phys.LM$DF<-round(TableS4_Phys.LM$DF, 0)

##Write Out Table
write.csv(TableS4_Phys.LM, "Tables/TableS4_Physiology_LM_Results.csv", row.names=FALSE)

```

